The energy in Vietnam’s cities is frenetic as millions of people navigate the busy streets with determination and purpose. Much of this traffic is driven by e-commerce, with food and parcel deliveries perched on the back of the country’s countless motorcycles or in cars and trucks. In the first quarter of 2024, online spending in Vietnam grew a staggering 79% over the previous year. Explosive growth like this is expected to continue, raising the industry’s value to $32 billion by 2025, with 70% of the country’s 100 million population making e-commerce transactions.

With massive numbers like this, in logistics, efficiency is king. The high customer expectations for rapid deliveries drive companies like Ahamove to innovate their way to seamless operations with cloud technology. Ahamove is Vietnam’s largest on-demand delivery company, handling more than 200,000 e-commerce, food, and warehouse deliveries daily, with 100,000 drivers and riders plying the streets nationwide. The logistics leader serves a network of more than 300,000 merchants, including regional e-commerce giants like Lazada and Shopee, as well as nationwide supermarket chains and small restaurants. The stakes are high for all involved, so maximizing efficiency is of utmost importance.

Innovating to make scale count

Online shoppers’ behavior is rarely predictable, and to cope with sudden spikes in daily delivery demand, Ahamove needed to efficiently scale up its operations to enhance customer and end-user satisfaction. Moving to MongoDB Atlas on Amazon Web Services (AWS) in 2019, Ahamove fundamentally changed its ability to meet the rising demand for deliveries and new services that please e-commerce providers, online shoppers, and diners.

The scalability of MongoDB is crucial for Ahamove, especially during peak times, like Christmas or Lunar New Year, when the volume of orders surges to more than 200,000 a day. “MongoDB's ability to scale ensures that the database can handle increased loads, including data requests, without compromising performance and leading to quicker order processing and improved user experience,” said Tien Ta, Strategic Planning Manager at Ahamove.

One of the powerful services that improves e-commerce across Vietnam is geospatial queries enabled by MongoDB. Using this geospatial data associated with specific locations on Earth's surface, Ahamove can easily locate drivers, map drivers to restaurants to accelerate deliveries, and track orders without relying on third-party services to provide information, which slows deliveries. Meanwhile, the versatility of MongoDB’s developer data platform empowers Ahamove to store its operational data, metadata, and vector embeddings on MongoDB Atlas and seamlessly use Atlas Vector Search to index, retrieve, and build performant generative artificial intelligence (AI) applications.

AI evolution

Powered by MongoDB Atlas, Ahamove is transforming Vietnam’s e-commerce industry with innovations like instant order matching, real-time GPS vehicle tracking, generative AI chatbots, and services like driver rating and variable delivery times, all available 24 hours a day, seven days a week.

In addition to traffic, Vietnam is also famous for its excellent street food. Recognizing the importance of the country’s rapidly growing food and beverage (F&B) industry, which is projected to be worth more than US$27.3 billion in 2024, Ahamove decided to help Vietnam’s small food vendors benefit from the e-commerce boom gripping the country. Using the latest models, including ChatGPT-4o-mini and Llama 3.1, Ahamove’s fully automated generative AI chatbot on MongoDB integrates with restaurants’ Facebook pages. This makes it easier for hungry consumers to handle the entire order process with the restaurant in natural language, from seeking recommendations to placing orders, making payments, and tracking deliveries to their doorsteps.

“Vietnam’s e-commerce industry is growing rapidly as more people turn to their mobile devices to purchase goods and services,” added Ta. “With MongoDB, we meet this customer need for new purchase experiences with innovative services like generative AI chatbots and faster delivery times.”

Anticipated to achieve 10% of food deliveries at Da Nang market and take the solution nationwide in the first half of 2025, AhaFood.AI - Ahamove’s latest initiative, also provides personalized dish recommendations based on consumer demographics, budgets, or historical preferences, helping people find and order their favorite food faster. Moreover, merchants receive timely notifications of incoming orders via the AhaMerchant web portal, allowing them to start preparing dishes earlier. AhaFood.AI also collects and securely stores users’ delivery addresses and phone numbers, ensuring better driver assignment and fulfilling food orders in less than 15 minutes.

“Adopting MongoDB Atlas was one of the best decisions we’ve ever made for Ahamove, allowing us to build an effective infrastructure that can scale with growing demand and deliver a better experience for our drivers and customers,” said Ngon Pham, CEO, Ahamove. “Generative AI will significantly disrupt the e-commerce and food industry, and with MongoDB Vector Search we can rapidly build new solutions using the latest database and AI technology.”

The vibrant atmosphere of Vietnam's bustling cities is part of the country's charm. Rather than seeking to bring calm to this energy, Vietnam thrives on it. Focusing on improving efficiency and supporting street food vendors in lively urban areas with cloud technology will benefit all.

Since we launched our System Integrators Certification Program in 2022, we have certified over 18,000 associates and architects across MongoDB’s various system integrator, advisory, and consulting services partners. This program gives system integrators a solid foundation in MongoDB and the capabilities that enable them to architect modernization projects and modern, AI-enriched applications.

Our customers continue to tell us that they are looking to innovate quicker and take advantage of new technologies, and we want to support them in these goals. They want to work with partners who have in-depth knowledge of the problems they are trying to solve and hands-on experience working with the technology they are implementing.

To meet this customer need and continue to evolve our partnership with our system integrators, we have launched the MongoDB Application Delivery Certification. This is a natural evolution of our certification program that provides comprehensive training and equips developers and application delivery leads with the knowledge and skills needed to design, develop, and deploy modern solutions at scale.

Driving innovation alongside our partners

The MongoDB Application Delivery Certification includes exclusive, partner-only, online learning and hands-on labs, as well as a proctored certification exam that teaches application delivery fundamentals and implementation best practices. Partners can expect carefully curated content on everything from optimizing storage, queries, and aggregation to retrieval-augmented generation (RAG), and how to architect and deliver with Atlas Vector Search.

We piloted this new program with our partners at Accenture and Capgemini to ensure it would drive value for all participants. Twenty developers were invited from each company to participate in an initial version of the curriculum and were able to provide their input on its content. Based on their feedback, we created a program that’s completely self-service and flexible, so learners can fit the coursework into their schedules, at their own pace.

"With the growth of AI and data-powered applications, Capgemini are investing in our staff to ensure they have the skills required for this transformation,” said Steve Jones, Executive Vice President, Data Driven Business & Collaborative Data Ecosystems at Capgemini. “The MongoDB Application Delivery Certification helps ensure our people have the right skills to help MongoDB and Capgemini collaborate with our clients on delivering the maximum business value possible in the data-powered future."

"Accenture, a strategic partner and part of MongoDB’s AI Application Program, leverages MongoDB’s certification program to ensure the highest quality of delivery capability as our clients race to modernize legacy systems to MongoDB,” said Ram Ramalingam, Senior Managing Director and Global Lead, Platform Engineering and Intelligent Edge at Accenture.

We understand that for many businesses, speed is a necessity, and keeping pace with the technological innovation in the current market is essential. Now, customers looking to implement MongoDB solutions will be able to do so quickly and easily by working with partners who have achieved the new MongoDB Application Delivery Certification. They can have the peace of mind knowing that these validated partners are extensively equipped to create and deploy robust MongoDB solutions at scale.

What’s more, this new certification will provide partners with other opportunities. Partners who have demonstrated deep technical expertise by successfully completing the MongoDB Application Delivery Certification Program may be considered for the MongoDB AI Applications Program (MAAP). This will give them access to a greater network of customers that need help building and deploying modern applications enriched with AI technology.

When connecting with customers, sales teams often struggle to understand and address the unique needs and preferences of each prospect, leading to ineffective pitches. Additionally, time-consuming admin tasks like data entry, sales tool updates, follow-up management, and maintaining personalized interactions across numerous leads can overwhelm teams, leaving less time for impactful selling.

Glyphic AI, a pioneering AI-powered sales co-pilot, addresses these challenges. By analyzing sales processes and calls, Glyphic AI helps teams streamline workflows and focus on building stronger customer relationships.

Founded by former engineers from Google DeepMind and Apple, Glyphic AI leverages expertise in large language models (LLMs) to work with private and dynamic data. "As LLM researchers, we discovered the true potential of these models lies in the sales domain, generating vast numbers of calls rich with untapped insights. Traditionally, these valuable insights were lost in digital archives, as extracting them required manually reviewing calls and making notes," says Devang Agrawal, co-Founder and Chief Technology Officer of Glyphic AI. “Our aim became to enhance customer centricity by harnessing AI to capture and utilize conversational and historical data, transforming it into actionable intelligence for ongoing and future deals.”

Built on MongoDB, AWS, and Anthropic, Glyphic AI automatically breaks down sales calls using established methodologies like MEDDIC. It leverages ingested sales playbooks to provide tailored strategies for different customer personas and company types. By using data sources such as Crunchbase, LinkedIn, and internal CRM information, the tool proactively surfaces relevant insights before sales teams engage with customers.

Glyphic AI employs LLMs to offer complete visibility into sales deals by understanding the full context and intent of real-time conversations. The system captures information at various points, primarily focusing on sales calls and recordings. These data are analyzed by LLMs tailored for sales tasks, summarizing content based on sales frameworks and extracting specific information requested by teams. MongoDB records serve as the main database for customer records, sales call data, and related metadata, while large video files are stored in AWS S3. MongoDB Atlas Search and Vector Search features are integrated, providing the ability to index and query high-dimensional vectors efficiently.

Glyphic AI’s Global Search feature uses Atlas Vector Search to allow users to ask strategic questions and retrieve data from numerous sales calls. It matches queries with vector embeddings in MongoDB, utilizing metadata, account details, and external sources like LinkedIn and Crunchbase to identify relevant content. This content is processed by the LLM model for detailed conversational responses. Additionally, MongoDB's Atlas Vector Search continuously updates records, building a dynamic knowledge base that provides quick insights and proactively generates summaries enriched with data from various sources, assisting with sales calls and customer analysis.

Why Glyphic AI relies on advanced cloud solutions for efficient data management and innovation

"I used MongoDB in the first app I ever built, and ever since it has consistently met our needs, no matter the project," says Agrawal.

For Glyphic AI, MongoDB has seamlessly integrated into the company’s existing workflows. MongoDB Atlas has greatly simplified database management and analytics, initially involving the team implementing vector search from scratch. When MongoDB introduced Atlas Vector Search, Glyphic AI transitioned to this more streamlined and integrated solution.

“If MongoDB's Atlas Vector Search had been available back then, we would have adopted it immediately for its ease of testing and deployment,” Agrawal reflects.

While Agrawal appreciates the benefits of building from scratch, he acknowledges that maintaining complex systems, like databases or developing LLM models, becomes increasingly challenging over time. The AI feature enabling natural language queries in MongoDB Compass has been particularly beneficial for Glyphic AI, especially when extracting insights not yet available in dashboards or analyzing specific database elements.

In the fast-paced AI industry, time to market is critical. MongoDB Atlas, as a cloud solution, offers Glyphic AI the flexibility and scalability needed to quickly test, deploy, and refine its applications. The integration of MongoDB Atlas with features like Atlas Vector Search has enabled the team to focus on innovation without being bogged down by infrastructure complexities, speeding up the development of AI-powered features.

As a small, agile team, Glyphic AI leverages MongoDB's document model, which aligns well with object-oriented programming principles. This allows for rapid development and iteration of product features, enabling the company to stay competitive in the evolving generative AI market. By simplifying data management and reducing friction, MongoDB’s document model helps Glyphic AI maintain agility and focus on delivering impactful solutions.

With vector search embedded in MongoDB, the team found relief in using a unified language and system. Keeping all data—including production records and vectors—in one place has greatly simplified operations.

Before adopting MongoDB, the team struggled with synchronizing data across multiple systems and managing deletions to avoid inconsistencies. MongoDB’s ACID compliance has made this process far more straightforward, ensuring reliable transactions and maintaining data integrity. By consolidating production records and vectors into MongoDB, the team achieved the simplicity they needed, eliminating the complexities of managing disparate systems.

Glyphic AI's next step: Refining LLMs for enhanced sales insights and strategic decision-making

“Over the next year, our goal is to refine our LLMs specifically for the sales context to deliver more strategic insights. We've built a strong conversational intelligence product that enhances efficiency for frontline sales reps and managers. Now, we're focused on aggregating conversation data to provide strategy teams and CROs with valuable insights into their teams' performance,” says Agrawal.

As sales analysis evolves to become more strategic, significant technical challenges will arise, especially when scaling from summarizing a handful of calls to analyzing thousands in search of complex patterns. Current LLMs are often limited in their ability to process large amounts of sales call data, which means ongoing adjustments and improvements will be necessary to keep up with new developments. Additionally, curating effective datasets, including synthetic and openly available sales data, will be a key hurdle in training these models to deliver meaningful insights.

By using MongoDB, Glyphic AI will be able to accelerate innovation due to the reduced need for time-consuming maintenance and management of complex systems. This will allow the team to focus on essential tasks like hiring skilled talent, driving innovation, and improving the end-user experience. As a result, Glyphic AI will be able to prioritize core objectives and continue to develop and refine their products effectively.

As Glyphic AI fine-tunes its LLMs for the sales context, the team will embrace retrieval-augmented generation (RAG) to push the boundaries of AI-driven insights. Leveraging Atlas Vector Search will enable Glyphic AI to handle large datasets more efficiently, transforming raw data into actionable sales strategies. This will enhance its AI’s ability to understand and predict sales trends with greater precision, setting the stage for a new level of sales intelligence and positioning Glyphic AI at the forefront of AI-driven sales solutions.

As part of the MongoDB AI Innovators Program, Glyphic AI’s engineers gain direct access to MongoDB’s product management team, facilitating feedback exchange and receiving the latest updates and best practices. This collaboration allows them to concentrate on developing their LLM models and accelerating application development. Additionally, the provision of MongoDB Atlas credits helps reduce costs associated with experimenting with new features.

Pathfinder Labs develops software that specializes in empowering law enforcement agencies and investigators to apprehend criminals and rescue victims of child abuse.

The New Zealand-headquartered company is staffed by professionals with diverse backgrounds and expertise, including counter-terrorism, online child abuse investigations, industrial espionage, digital forensics and more, spanning both the government and private sectors.

Last July, I was thrilled to welcome Pathfinder Labs’ CEO Bree Atkinson, as well as co-founder and DevOps Architect, Peter Pilley to MongoDB .local Sydney where they shared more about the company’s innovative solutions powered by MongoDB.

Those solutions are deployed and utilized by prestigious organizations on a global scale, including Interpol.

Pathfinder Labs’ main product, Paradigm, has been built on MongoDB Atlas and runs on AWS. The tool—which relies on MongoDB’s developer data platform and document database model to sift through complex and continually growing numbers of data sets—helps collect, gather, and convert data into actionable decisions for law enforcement professionals.

Pilley explained that Paradigm was “made by investigators, for investigators.”

Paradigm is designed to present the information it helps gather in a way that will support a successful prosecution and outcome at trial.

MongoDB Atlas enables Pathfinder Labs to tame the chaos arising from the data sets created and gathered throughout an investigation. MongoDB’s scalability and automation capabilities are particularly helpful in this regard.

Powered by MongoDB Atlas, Paradigm can also easily identify similarities between cases, and uncover unique insights by bringing together information from disparate data sources. This could, for example, be about bringing together geolocalization data and metadata from an image, or identifying similar case patterns from law enforcement agencies operating in different states or countries.

Ultimately, Paradigm simplifies evidence gathering and analysis, integrates external data sources and vendors, future-proof investigation methods, and helps minimize overall costs. Its capabilities are unlocking a whole new generation of data-driven investigative capabilities.

During the presentation, Pilley used the example of the case of a missing female in the United States: it took a team of three investigators 12 months to solve the case. Using Paradigm, PathfinderLabs was able to solve that same case in less than an hour.

“With Paradigm, we were able to feed some extra information and solve the case in 40 minutes. MongoDB Atlas allowed us to make quick decisions and present information to investigators in the most efficient way.”

Pathfinder Labs also incorporates AI capabilities, including MongoDB Vector Search, which help identify which information is particularly relevant, select specific data points that can be used at a strategic point in time, connect data from one case to another, and identify what information might be missing.

MongoDB Atlas Vector Search helps Pathfinder match images and details in images (i.e. people, objects), classify documents and text, and to build better search experiences for users via semantic search.

“I was super excited when [Atlas Vector Search] came out. The fact that I can now have it as part of my standard workflow without having to deploy other kits all the time to support our vector searches has been an absolute game changer,” added Pilley.

Finally, the team has seen great value in MongoDB’s Performance Adviser and Schema Anti Patterns features: “The performance Adviser alone has solved many problems,” concluded Pilley.

Welcome to our article series focused on developers and what they do when they’re not building incredible things with code and data.

“Away From the Keyboard” features interviews with developers at MongoDB, discussing what they do, how they establish a healthy work-life balance, and their advice for others looking to create a more holistic approach to coding.

In this article, Apoorva Joshi shares her day-to-day responsibilities as a Senior AI Developer Advocate at MongoDB; what a flexible approach to her job and life looks like; and how her work calendar helps prioritize overall balance.

Q: What do you do at MongoDB?

Apoorva: My job is to help developers successfully build AI applications using MongoDB. I do this through written technical content, hands-on workshops, and design whiteboarding sessions.

Q: What does work-life balance look like for you?

Apoorva: I love remote work. It allows me to have a flexible approach towards work and life where I can accommodate life things, like dental appointments, walks, or lunches in the park during my work day—as long as work gets done.

Q: Was that balance always a priority for you or did you develop it later in your career?

Apoorva: Making work-life balance a priority has been a fairly recent development. During my first few years on the job, I would work long hours, partly because I felt like I needed to prove myself and also because I hadn’t prioritized finding activities I enjoyed outside of school or work up until then.

The first lockdown during the pandemic put a lot of things into perspective. With work and life happening in the same place, I felt the need for boundaries. Having nowhere to go encouraged me to try out new hobbies, such as solving jigsaw puzzles; as well as reconnecting with old favorites, like reading and painting.

Q: What benefits has this balance given you?

Apoorva: Doing activities away from the keyboard makes me more productive at work. A flexible working schedule also creates a stress-free environment and allows me to bring my 100% to work. This balance helps me make time for family and friends, exercise, chores, and hobbies. Overall, having a healthy work-life balance helps me lead a fulfilling life that I am proud of.

Q: What advice would you give to a developer seeking to find a better balance?

Apoorva: The first step to finding a balance between work and life is to recognize that boundaries are healthy. I have found that putting everyday things, such as lunch breaks and walks on my work calendar is a good way to remind myself to take that break or close my laptop, while also communicating those boundaries with my colleagues. If you are having trouble doing this on your own, ask a family member, partner, or friend to remind you!

Thank you to Apoorva Joshi for sharing her insights! And thanks to all of you for reading. Look for more in our new series.

Aftersales service is a crucial revenue stream for the automotive industry, with leading manufacturers executing repairs through their dealer networks. One global automotive giant recently embarked on an ambitious project to revolutionize their diagnostic process. Their project—which aimed to increase efficiency, customer satisfaction, and revenue throughput—involved the development of an AI-powered solution that could quickly analyze engine sounds and compare them to a database of known problems, significantly reducing diagnostic times for complex engine issues.

Traditional diagnostic methods can be time-consuming, expensive, and imprecise, especially for complex engine issues. MongoDB’s client in automotive manufacturing envisioned an AI-powered solution that could quickly analyze engine sounds and compare them to a database of known problems, significantly reducing diagnostic times.

Initial setbacks, then a fresh perspective

Despite the client team's best efforts, the project faced significant challenges and setbacks during the nine-month prototype phase. Though the team struggled to produce reliable results, they were determined to make the project a success.

At this point, MongoDB introduced its client to Pureinsights, a specialized gen AI implementation and MongoDB AI Application Program partner, to rethink the solution and to salvage the project. As new members of the project team, and as PureInsights’s CTO and Lead Architect, respectively, we brought a fresh perspective to the challenge.

A pragmatic approach: Text before sound

Upon review, we discovered that the project had initially started with a text-based approach before being persuaded to switch to sound analysis. The PureInsights team recommended reverting to text analysis as a foundational step before tackling the more complex audio problem.

This strategy involved:

1. Collecting text descriptions of car problems from technicians and customers.

2. Comparing these descriptions against a vast database of known issues already stored in MongoDB.

3. Utilizing advanced natural language processing, semantic / vector search, and Retrieval Augmented Generation techniques to identify similar cases and potential solutions.

Our team tested six different models for cross-lingual semantic similarity, ultimately settling on Google's Gecko model for its superior performance across 11 languages.

Pushing boundaries: Integrating audio analysis

With the text-based foundation in place, we turned to audio analysis. Pureinsights developed an innovative approach to the project by combining our AI expertise with insights from advanced sound analysis research. We drew inspiration from groundbreaking models that had gained renown for their ability to identify cities solely from background noise in audio files.

This blend of AI knowledge and specialized audio analysis techniques resulted in a robust, scalable system capable of isolating and analyzing engine sounds from various recordings. We adapted these sophisticated audio analysis models, originally designed for urban sound identification, to the specific challenges of automotive diagnostics. These learnings and adaptations are also applicable to future use cases for AI-driven audio analysis across various industries.

This expertise was crucial in developing a sophisticated audio analysis model capable of:

1. Isolating engine and car noises from customer or technician recordings.

2. Converting these isolated sounds into vectors.

3. Using these vectors to search the manufacturer's existing database of known car problem sounds.

At the heart of this solution is MongoDB’s powerful database technology. The system leverages MongoDB’s vector and document stores to manage over 200,000 case files. Each "document" is more akin to a folder or case file containing:

• Structured data about the vehicle and reported issue

• Sound samples of the problem

• Unstructured text describing the symptoms and context

This unified approach allows for seamless comparison of text and audio descriptions of customer engine problems using MongoDB's native vector search technology.

Encouraging progress and phased implementation

The solution's text component has already been rolled out to several dealers, and the audio similarity feature will be integrated in late 2024. This phased approach allows for real-world testing and refinement before a full-scale deployment across the entire repair network.

The client is taking a pragmatic, step-by-step approach to implementation. If the initial partial rollout with audio diagnostics proves successful, the plan is to expand the solution more broadly across the dealer network. This cautious (yet forward-thinking) strategy aligns with the automotive industry's move towards more data-driven maintenance practices.

As the solution continues to evolve, the team remains focused on enhancing its core capabilities in text and audio analysis for current diagnostic needs. The manufacturer is committed to evaluating the real-world impact of these innovations before considering potential future enhancements. This measured approach ensures that each phase of the rollout delivers tangible benefits in efficiency, accuracy, and customer satisfaction.

By prioritizing current diagnostic capabilities and adopting a phased implementation strategy, the automotive giant is paving the way for a new era of efficiency and customer service in their aftersales operations. The success of this initial rollout will inform future directions and potential expansions of the AI-powered diagnostic system.

A new era in automotive diagnostics

The automotive giant brought industry expertise and a clear vision for improving their aftersales service. MongoDB provided the robust, flexible data platform essential for managing and analyzing diverse, multi-modal data types at scale. We, at Pureinsights, served as the AI application specialist partner, contributing critical AI and machine learning expertise, and bringing fresh perspectives and innovative approaches. We believe our role was pivotal in rethinking the solution and salvaging the project at a crucial juncture.

This synergy of strengths allowed the entire project team to overcome initial setbacks and develop a groundbreaking solution that combines cutting-edge AI technologies with MongoDB's powerful data management capabilities. The result is a diagnostic tool leveraging text and audio analysis to significantly reduce diagnostic times, increase customer satisfaction, and boost revenue through the dealer network.

The project's success underscores several key lessons:

1. The value of persistence and flexibility in tackling complex challenges

2. The importance of choosing the right technology partners

3. The power of combining domain expertise with technological innovation

4. The benefits of a phased, iterative approach to implementation

As industries continue to evolve in the age of AI and big data, this collaborative model—bringing together industry leaders, technology providers, and specialized AI partners—sets a new standard for innovation. It demonstrates how companies can leverage partnerships to turn ambitious visions into reality, creating solutions that drive business value while enhancing customer experiences.

The future of automotive diagnostics—and AI-driven solutions across industries—looks brighter thanks to the combined efforts of forward-thinking enterprises, cutting-edge database technologies like MongoDB, and specialized AI partners like Pureinsights. As this solution continues to evolve and deploy across the global dealer network, it paves the way for a new era of efficiency, accuracy, and customer satisfaction in the automotive industry. This solution has the potential to not only revolutionize automotive diagnostics but also set a new standard for AI-driven solutions in other industries, demonstrating the power of collaboration and innovation.

How do you bring the benefits of gen AI, a technology typically experienced on a keyboard and screen, into the physical world?

That's the problem the team at Ramblr.ai, a San Francisco-based startup, is solving with its powerful and versatile 3D annotation and recognition capabilities.

“With Ramblr you can record continuously what you are doing, and then ask the computer, in natural language, ‘Where did I go wrong’ or ‘What should I do next?” said Frank Angermann, Lead Pipeline & Infrastructure Engineer at Ramblr.ai.

Gen AI for the real world

One of the best examples of Ramblr’s technology, and its potential, is its work with the international chemical giant BASF.

In a video demonstration on Ramblr’s website, a BASF engineer can be seen tightening bolts on a connector (or ‘flange’) joining two parts of a pipeline. Every move the engineer makes is recorded via a helmet-mounted camera. Once the worker is finished for the day this footage, and the footage of every other person working on the pipeline, is uploaded to a database.

Using Ramblr’s technology, quality assurance engineers from BASF then query the collected footage from every worker, asking the software to, ‘Please assess footage from today’s pipeline connection work and see if any of the bolts were not tightened enough.’ Having processed the footage, Ramblr assesses whether those flanges had been assembled correctly and identifies any that required further inspection or correction.

The method behind the magic

“We started Ramblr.ai as an annotation platform, a place where customers could easily label images from a video and have machine learning models then identify that annotation throughout the video automatically,” said Frank.

“In the past this work would be carried out manually by thousands of low-paid workers tagging videos by hand. We thought we could be better by automating that process,” he added.

The software allows customers to easily customize and add annotations to footage for their particular use case, and with its gen-AI powered active learning approach Ramblr then ‘fills in’ the rest of the video based on those annotations.

Why MongoDB?

MongoDB has been part of the Ramblr technology stack since the beginning.

“We use MongoDB Atlas for half of our storage processes. Metadata, annotation data, etc., can all be stored in the same database. This means we don’t have to rely on separate databases to store different types of data,” said Frank.

Flexibility of data storage was also a key consideration when choosing a database. “With MongoDB Atlas, we could store information the way we wanted to,” he added.

The built-in vector database capabilities of Atlas were also appealing to the Rambler team, “The ability to store vector embeddings without having to do any more work - for instance not having to move a 3mb array of data somewhere else to process it, was a big bonus for us.”

The future

Aside from infrastructure and construction Q&A, robotics is another area in which the Ramblr team is eager to deploy their technology.

“Smaller robotics companies don’t typically have the data to train the models that inform their products. There are quite a few use cases where we could support these companies and provide a more efficient and cost-effective way to teach the robots more efficiently. We are extremely efficient in providing information for object detectors,” said Frank.

But while there are plenty of commercial uses for Ramblr’s technology, the growth in spatial computing in the consumer sector - especially following the release of Apple’s Vision Pro and Meta Quest headsets - opens up a whole new category of use cases.

“Spatial computing will be a big part of the world. Being able to understand the particular processes, taxonomy, and what the person is actually seeing in front of them will be a vital part of the next wave of innovation in user interfaces and the evolution of gen AI,” Frank added.

I recently received an automated reminder that I was approaching a work anniversary, which took me somewhat by surprise. It’s hard to believe that it’s already been a year (to the day) that I joined MongoDB! So I thought I’d take a moment to reflect on my MongoDB journey so far, share some exciting product updates, and signal where we’re headed next.

Our customers

I joined MongoDB because it built a product developers love. The innovation of MongoDB’s document model empowered developers to simply build. No longer encumbered by having to formalize and denormalize their data schema before their application was even designed, MongoDB enabled developers to interact with data in an intuitive JSON format, and made it easy to evolve data structures as the life of their application evolved.

One of my first steps upon joining the company was to learn more about our customers. I was excited to learn that in addition to delighting developers, MongoDB had launched capabilities that enabled it to win mission-critical workloads from enterprise class customers—including 70% of the Fortune 100 and highly regulated global financial institutions, health care providers, and government agencies. I found it remarkable that customers could replicate data across AWS, Google Cloud, and Microsoft Azure in MongoDB Atlas (our fully-managed cloud database service) with just a few mouse clicks, and that some customers replicate data between the cloud and on premises using MongoDB Enterprise Advanced. This optionality struck me as powerful in the era of rapid advancements in AI, as it enables customers to easily bring their data to the best cloud provider for AI.

Soon after I joined MongoDB, the team was firming up the development roadmap for the next version of MongoDB, and they asked for my input on the plan. The team was debating whether to focus on features developers would love, or governance capabilities required by large enterprises. I knew that ideally we would please all of our customers, so we had to try to make this an “and” and not an “or.” While I was new to MongoDB, from my 17+ years at AWS I learned that all customers demand security, durability, availability, and performance (in that order) from any modern technology offering. If a product or service doesn’t have those four elements, customers won’t buy whatever you’re selling. So as a team, we agreed that our next release of MongoDB—MongoDB 8.0—had to raise the bar for all of our customers, delivering great security, durability, availability, and performance.

The plan

We had less than a year before our target launch, so we knew we had to get moving, fast. My team and I brought MongoDB’s product and engineering organizations together to align on the plan for our next release. We set goals around delivering significant improvements in security, durability, and availability. And we set a line in the sand—that we weren’t going to release MongoDB 8.0 unless it was the best-performing version of MongoDB yet.

Measuring the performance of a feature-rich database like MongoDB can be tricky, as customers run a wide range of workloads. So we decided to run a suite of benchmarks to simulate customer workloads. We also developed Andon cord-inspired automation that would automatically roll back any code contributions that regressed our performance metrics. Finally, a set of senior engineering leaders met regularly to review our progress and immediately escalated any blockers that could jeopardize our launch, so that we could quickly fix things.

From my experience, I knew that great teams really respond when they’re given clear goals, and when they’re empowered to innovate, so I was excited to see what they would come up with. I’m proud to say that our product and engineering teams rose to the challenge.

Announcing MongoDB 8.0

Today, I’m thrilled to announce the general availability of MongoDB 8.0—the most secure, durable, available, and performant version of MongoDB yet! The team came up with architectural optimizations in MongoDB 8.0 that have significantly reduced memory usage and query times, and have made batch processing more efficient than previous versions.

Specifically, MongoDB 8.0 features:

• 36% better read throughput

• 56% faster bulk writes

• 20% faster concurrent writes during data replication

• 200% faster on complex aggregations of times series data

In making these improvements, we're seeing benchmarks for typical web applications perform 32% better overall. Here’s a breakdown of how MongoDB 8.0 performs against some of our benchmarks:

Improved performance benefits all users of applications built atop MongoDB, and for MongoDB customers, it can mean reduced costs (due to an improved price/performance ratio).

In addition to significant performance gains, MongoDB 8.0 delivers a wide range of improvements, including (but not limited to):

• Improving availability by delivering sharding enhancements to distribute data across shards up to 50 times faster and at up to 50% lower starting cost, with reduced need for additional configuration or setup.

• Improving support for a wide range of search and AI applications at higher scale and lower cost, via the delivery of quantized vectors—compressed representations of full-fidelity vectors—that require up to 96% less memory and are faster to retrieve while preserving accuracy.

• Enabling customers to encrypt data at rest, in transit, and in use by expanding MongoDB’s Queryable Encryption to also support range queries. Queryable Encryption is a groundbreaking, industry-first innovation developed by the MongoDB Cryptography Research Group that allows customers to encrypt sensitive application data, store it securely as fully randomized encrypted data in the MongoDB database, and run expressive queries on the encrypted data—with no cryptography expertise required.

You might wonder why we’re so confident that customers are going to love MongoDB 8.0. Well, we’ve been acting as our own customer, and have moved our own applications over to 8.0. This approach is generally called “dogfooding,” but we think that “eating our own pizza” sounds more appetizing. Our internal build system—which our software developers use daily—is built atop MongoDB, and when we upgraded to MongoDB 8.0 we saw query latencies drop by approximately 75%! This was a double win, as it improved the performance of our own tooling, and it set our performance chat room abuzz with excitement in anticipation of delighting external customers. While results may vary based on your particular workload, the point is that we just couldn’t wait to share MongoDB 8.0’s performance gains with customers.

Indeed, customers are also already seeing great results on MongoDB 8.0. For example, Felix Horvat, Chief Technology Officer at OCELL, a climate technology company in Germany, said: “With MongoDB 8.0, we have seen an incredible boost in performance, with some of our queries running twice as fast as before. This improvement not only enhances our data processing capabilities but also aligns perfectly with our commitment to resource efficiency. By optimizing our backend operations, we can be more effective in our climate initiatives while conserving resources—a true reflection of our dedication to sustainable solutions.”

I encourage you to check out MongoDB 8.0 yourself. It’s available today via MongoDB Atlas, as part of MongoDB Enterprise Advanced for on-premises and hybrid deployments, and as a free download from mongodb.com/try with MongoDB Community Edition. In addition, customers upgrading from previous versions of MongoDB to 8.0 can find helpful upgrade guides on mongodb.com.

What’s next?

We’re excited for you to try MongoDB 8.0 and to share your feedback, as customer feedback helps us guide our roadmap for future releases.

Going forward, please watch this space. Over the next few weeks, we’ll be publishing a series of engineering blog posts that dig into MongoDB’s investments in the technology behind MongoDB 8.0. We’re also planning posts about horizontal scaling in MongoDB 8.0, and one that will look closely at queryable encryption (QE), but let me know what you’d like to hear more about.

It’s been an exciting year at MongoDB—I can’t wait to see what the next one has in store!

–Jim

We’re excited to announce that MongoDB 8.0—the newest version of the world’s most popular document database, used by millions of developers and more than 50,000 customers around the world—is now generally available. MongoDB 8.0 builds upon MongoDB’s industry-leading capabilities to provide significant performance improvements, reduced costs, and greater ease of use, from local deployments to globally distributed applications at enterprise scale.

Developers have long loved building with MongoDB, so we've ensured that 8.0 kept the bar extremely high for developer usability. MongoDB 8.0 was also built to exceed our customers’ most stringent security, resiliency, availability, and performance requirements, and is the most impressive version of MongoDB yet. MongoDB 8.0 gives customers the strongest possible foundation for building a wide range of applications, now and in the future.

Jim Scharf, Chief Technology Officer, MongoDB

For MongoDB 8.0, we focused our engineering efforts around four core goals:

• Optimize performance for the widest variety of applications

• Deliver innovative encryption to unlock new use cases

• Reduce costs and increase scale with rapid and intuitive horizontal scaling for high availability

• Ensure resilience for unexpected application demand

So how do these goals actually benefit teams as they build and manage applications? We’ll start by looking at why you should use MongoDB 8.0.

Whether you’re a seasoned MongoDB veteran or are new to the database, MongoDB 8.0 is a great foundation for new applications and supercharging existing ones alike. Version 8.0 combines the things developers love most about MongoDB—like an intuitive and cohesive developer experience, support for a broad set of use cases, and operational ease of use—with unparalleled performance improvements.

Top reasons to switch to MongoDB 8.0

1. MongoDB 8.0 is over 30% faster than before

As the data applications generate and use grows, minor inefficiencies can lead to disproportionate increases in infrastructure costs. Because many customers primarily interact with businesses through their applications, poor or inconsistent application performance can lead to customer unhappiness, lost opportunities, and declines in revenue. So it’s imperative for organizations to ensure that their applications perform consistently well.

MongoDB 8.0 significantly improves performance by allowing applications to rapidly and efficiently query and transform data, with up to 36% better throughput. Architectural optimizations in MongoDB 8.0 have reduced memory usage and query times, and a combination of more efficient batch processing and optimizations has enabled 56% faster bulk writes and 20% faster concurrent writes during data replication. Additionally, optimizations in MongoDB 8.0 mean the database can handle higher volumes of time series data and perform operations over 200% faster—with lower resource usage and costs.

2. MongoDB 8.0 is more secure than ever

Data protection and security are essential. With the increasing complexity and volume of data being transmitted, stored, and processed across environments, safeguarding sensitive information with robust encryption is more critical than ever. Organizations must protect their data throughout its lifecycle—in transit over networks, at rest where it is stored, and while it’s in use for querying and processing. However, it can be challenging to encrypt data while it is queried and processed, leaving data vulnerable to exposure or exfiltration by malicious actors.

MongoDB Queryable Encryption is an industry-first innovation developed by the MongoDB Cryptography Research Group. It allows customers to encrypt sensitive data on the client side, store it securely as fully randomized encrypted data in the MongoDB database, and to run expressive queries on the encrypted data for processing.

MongoDB 8.0 now includes support for range queries—in addition to equality queries—to expand secure data retrieval with greater flexibility for common searches. With Queryable Encryption, the required data remains encrypted until it reaches an authorized end user using a customer-controlled decryption key—with no cryptography expertise required.

3. MongoDB 8.0 makes it cheaper and easier to scale

As organizations grow, their applications’ requirements tend to evolve. For example, scaling to support millions of users can be challenging for organizations that originally designed their applications for thousands of users. This is because implementing architectural changes in production applications can involve significant effort that can be costly and time-consuming.

With MongoDB 8.0, horizontal scaling is now faster and easier, and at a lower cost. With horizontal scaling, applications can scale beyond the limits of traditional database resources by splitting data across multiple servers known as shards—without having to pre-provision increasing amounts of compute resources for a single server. New sharding capabilities in MongoDB 8.0 distribute data across shards up to 50 times faster and at up to 50% lower cost to get started.

4. MongoDB 8.0 gives you more control to help your applications run smoothly

End-users expect consistent application experiences, even during periods of high demand and usage spikes. Organizations without a highly durable operational database risk poor customer experiences, with lagging application behavior (or even downtime) during times of high demand.

MongoDB 8.0 provides greater control for teams optimizing database performance for unpredictable spikes in usage and sustained periods of high demand. MongoDB 8.0 includes new capabilities to set a default maximum time limit for running queries, to reject recurring types of problematic queries, and to set query settings to persist through events like database restarts. These capabilities help deliver consistent application behavior and high performance, irrespective of demand spikes or unexpected events.

Ready to try MongoDB 8.0?

If you are building a new application, the easiest way to get started with MongoDB 8.0 is by going to mongodb.com/try, where you can sign up for a free Atlas account, download the Community edition, and learn more about self-managing MongoDB with an Enterprise Advanced subscription.

If you are running a previous version of MongoDB, there are helpful upgrade tutorials for MongoDB Atlas and self-managed deployments. Additionally, documentation and expert help from the MongoDB professional services team are on hand.

If you have an existing application that is not currently using MongoDB as the database, check out the MongoDB Relational Migrator tool. Relational Migrator can help you map existing relational schemas to a MongoDB schema, perform data migrations, and convert existing relational queries, triggers, and stored procedures to work with MongoDB.

The MongoDB engineering and product teams listened attentively to developer feedback, and MongoDB 8.0 was built with developer usability—as well as security, durability, availability, and performance—top of mind. We’re excited for you to give it a try, and are sure you’ll enjoy the performance gains and other benefits of MongoDB 8.0!

Since we kicked off MongoDB’s series of 2024 events in April, we’ve connected with thousands of customers, partners, and community members in cities around the world—from Mexico City to Mumbai. Yesterday marked the nineteenth stop of the 2024 MongoDB.local tour, and we had a blast welcoming folks across industries to MongoDB.local London, where we discussed the latest technology trends, celebrated customer innovations, and unveiled product updates that make it easier than ever for developers to build next-gen applications.

Over the past year, MongoDB’s more than 50,000 customers have been telling us that their needs are changing. They’re increasingly focused on three areas:

1. Helping developers build faster and more efficiently

2. Empowering teams to create AI-powered applications

3. Moving from legacy systems to modern platforms

Across these areas, there’s a common need for a solid foundation: each requires a resilient, scalable, secure, and highly performant database.

The updates we shared at MongoDB.local London reflect these priorities. MongoDB is committed to ensuring that our products are built to exceed our customers’ most stringent requirements, and that they provide the strongest possible foundation for building a wide range of applications, now and in the future.

Indeed, during yesterday’s event, Sahir Azam, MongoDB’s Chief Product Officer, discussed the foundational role data plays in his keynote address. He also shared the latest advancement from our partner ecosystem, an AI solution powered by MongoDB, Amazon Web Services, and Anthropic that makes it easier for customers to deploy gen AI customer care applications.

MongoDB 8.0: The best version of MongoDB ever

The biggest news at .local London was the general availability of MongoDB 8.0, which provides significant performance improvements, reduced scaling costs, and adds additional scalability, resilience, and data security capabilities to the world’s most popular document database.

Architectural optimizations in MongoDB 8.0 have significantly reduced memory usage and query times, and MongoDB 8.0 has more efficient batch processing capabilities than previous versions. Specifically, MongoDB 8.0 features 36% better read throughput, 56% faster bulk writes, and 20% faster concurrent writes during data replication. In addition, MongoDB 8.0 can handle higher volumes of time series data and can perform complex aggregations more than 200% faster—with lower resource usage and costs. Last (but hardly least!), Queryable Encryption now supports range queries, ensuring data security while enabling powerful analytics.

For more on MongoDB.local London’s product announcements—which are designed to accelerate application development, simplify AI innovation, and speed developer upskilling—please read on!

Accelerating application development

Improved scaling and elasticity on MongoDB Atlas capabilities

New enhancements to MongoDB Atlas’s control plane allow customers to scale clusters faster, respond to resource demands in real-time, and optimize performance—all while reducing operational costs.

First, our new granular resource provisioning and scaling features—including independent shard scaling and extended storage and IOPS on Azure—allow customers to optimize resources precisely where needed. Second, Atlas customers will experience faster cluster scaling with up to 50% quicker scaling times by scaling clusters in parallel by node type.

Finally, MongoDB Atlas users will enjoy more responsive auto-scaling, with a 5X improvement in responsiveness thanks to enhancements in our scaling algorithms and infrastructure. These enhancements are being rolled out to all Atlas customers, who should start seeing benefits immediately.

IntelliJ plugin for MongoDB

Announced in private preview, the MongoDB for IntelliJ Plugin is designed to functionally enhance the way developers work with MongoDB in IntelliJ IDEA, one of the most popular IDEs among Java developers. The plugin allows enterprise Java developers to write and test Java queries faster, receive proactive performance insights, and reduce runtime errors right in their IDE.

By enhancing the database-to-IDE integration, JetBrains and MongoDB have partnered to deliver a seamless experience for their shared user-base and unlock their potential to build modern applications faster. Sign up for the private preview here.

MongoDB Copilot Participant for VS Code (Public Preview)

Now in public preview, the new MongoDB Participant for GitHub Copilot integrates domain-specific AI capabilities directly with a chat-like experience in the MongoDB Extension for VS Code.

The participant is deeply integrated with the MongoDB extension, allowing for the generation of accurate MongoDB queries (and exporting them to application code), describing collection schemas, and answering questions with up-to-date access to MongoDB documentation without requiring the developer to leave their coding environment. These capabilities significantly reduce the need for context switching between domains, enabling developers to stay in their flow and focus on building innovative applications.

Multicluster support for the MongoDB Enterprise Kubernetes Operator

Ensure high availability, resilience, and scale for MongoDB deployments running in Kubernetes through added support for deploying MongoDB and Ops Manager across multiple Kubernetes clusters.

Users now have the ability to deploy ReplicaSets, Sharded Clusters (in public preview), and Ops Manager across local or geographically distributed Kubernetes clusters for greater deployment resilience, flexibility, and disaster recovery. This approach enables multi-site availability, resilience, and scalability within Kubernetes, capabilities that were previously only available outside of Kubernetes for MongoDB. To learn more, check out the documentation.

MongoDB Atlas Search and Vector Search are now generally available via the Atlas CLI and Docker

The local development experience for MongoDB Atlas is now generally available. Use the MongoDB Atlas CLI and Docker to build with MongoDB Atlas in your preferred local environment, and easily access features like Atlas Search and Atlas Vector Search throughout the entire software development lifecycle. The Atlas CLI provides a unified and familiar terminal-based interface that allows you to deploy and build with MongoDB Atlas in your preferred development environment, locally or in the cloud.

If you build with Docker, you can also now use Docker and Docker Compose to easily integrate Atlas in your local and continuous integration environments with the Atlas CLI. Avoid repetitive work by automating the lifecycle of your development and testing environments and focus on building application features with full-text search, AI and semantic search, and more.

Simplifying AI innovation

Reduce costs and increase scale in Atlas Vector Search

We announced vector quantization capabilities in Atlas Vector Search. By reducing memory (by up to 96%) and making vectors faster to retrieve, vector quantization allows customers to build a wide range of AI and search applications at higher scale and lower cost.

Generally available now, support for scalar quantized vector ingestion lets customers seamlessly import and work with quantized vectors from their embedding model providers of choice—directly in Atlas Vector Search. Coming soon, additional vector quantization features, including automatic quantization, will equip customers with a comprehensive toolset for building and optimizing large-scale AI and search applications in Atlas Vector Search.

Additional integrations with popular AI frameworks

Ship your next AI-powered project faster with MongoDB, no matter your framework or LLM of choice. AI technologies are advancing rapidly, making it important to build and scale performant applications quickly, and to use your preferred stack as your requirements and available technologies evolve.

MongoDB’s enhanced suite of integrations with LangChain, LlamaIndex, Microsoft Semantic Kernel, AutoGen, Haystack, Spring AI, the ChatGPT Retrieval Plugin, and more make it easier than ever to build the next generation of applications on MongoDB.

Advancing developer upskilling

New MongoDB Learning Badges

Faster to achieve and more targeted than a certification, MongoDB's free Learning Badges show your commitment to continuous learning and to proving your knowledge about a specific topic. Follow the learning path, gain new skills, and get a digital badge to show off on LinkedIn.

Check out the two new gen AI learning badges!

• Building gen AI Apps: Learn to create innovative gen AI apps with Atlas Vector Search, including retrieval-augmented generation (RAG) apps.

• Deploying and Evaluating gen AI Apps: Take your apps from creation to full deployment, focusing on optimizing performance and evaluating results.

Learn more

To learn more about MongoDB’s recent product announcements and updates, check out our What’s New product announcements page and all of our blog posts about product updates. Happy building!

We are excited to announce a robust set of vector quantization capabilities in MongoDB Atlas Vector Search. These capabilities will reduce vector sizes while preserving performance, enabling developers to build powerful semantic search and generative AI applications with more scale—and at a lower cost. In addition, unlike relational or niche vector databases, MongoDB’s flexible document model—coupled with quantized vectors—allows for greater agility in testing and deploying different embedding models quickly and easily.

Support for scalar quantized vector ingestion is now generally available, and will be followed by several new releases in the coming weeks. Read on to learn how vector quantization works and visit our documentation to get started!

The challenges of large-scale vector applications

While the use of vectors has opened up a range of new possibilities, such as content summarization and sentiment analysis, natural language chatbots, and image generation, unlocking insights within unstructured data can require storing and searching through billions of vectors—which can quickly become infeasible.

Vectors are effectively arrays of floating-point numbers representing unstructured information in a way that computers can understand (ranging from a few hundred to billions of arrays), and as the number of vectors increases, so does the index size required to search over them. As a result, large-scale vector-based applications using full-fidelity vectors often have high processing costs and slow query times, hindering their scalability and performance.

Vector quantization for cost-effectiveness, scalability, and performance

Vector quantization, a technique that compresses vectors while preserving their semantic similarity, offers a solution to this challenge. Imagine converting a full-color image into grayscale to reduce storage space on a computer. This involves simplifying each pixel's color information by grouping similar colors into primary color channels or "quantization bins," and then representing each pixel with a single value from its bin. The binned values are then used to create a new grayscale image with smaller size but retaining most original details, as shown in Figure 1.

Vector quantization works similarly, by shrinking full-fidelity vectors into fewer bits to significantly reduce memory and storage costs without compromising the important details. Maintaining this balance is critical, as search and AI applications need to deliver relevant insights to be useful.

Two effective quantization methods are scalar (converting a float point into an integer) and binary (converting a float point into a single bit of 0 or 1). Current and upcoming quantization capabilities will empower developers to maximize the potential of Atlas Vector Search.

The most impactful benefit of vector quantization is increased scalability and cost savings through reduced computing resources and efficient processing of vectors. And when combined with Search Nodes—MongoDB’s dedicated infrastructure for independent scalability through workload isolation and memory-optimized infrastructure for semantic search and generative AI workloads— vector quantization can further reduce costs and improve performance, even at the highest volume and scale to unlock more use cases.

"Cohere is excited to be one of the first partners to support quantized vector ingestion in MongoDB Atlas,” said Nils Reimers, VP of AI Search at Cohere. “Embedding models, such as Cohere Embed v3, help enterprises see more accurate search results based on their own data sources. We’re looking forward to providing our joint customers with accurate, cost-effective applications for their needs.”

In our tests, compared to full-fidelity vectors, BSON-type vectors—MongoDB’s JSON-like binary serialization format for efficient document storage—reduced storage size by 66% (from 41 GB to 14 GB). And as shown in Figures 2 and 3, the tests illustrate significant memory reduction (73% to 96% less) and latency improvements using quantized vectors, where scalar quantization preserves recall performance and binary quantization’s recall performance is maintained with rescoring–a process of evaluating a small subset of the quantized outputs against full-fidelity vectors to improve the accuracy of the search results.

In addition, thanks to the reduced cost advantage, vector quantization facilitates more advanced, multiple vector use cases that would have been too computationally-taxing or cost-prohibitive to implement. For example, vector quantization can help users:

• Easily A/B test different embedding models using multiple vectors produced from the same source field during prototyping. MongoDB’s document model—coupled with quantized vectors—allows for greater agility at lower costs. The flexible document schema lets developers quickly deploy and compare embedding models’ results without the need to rebuild the index or provision an entirely new data model or set of infrastructure.

• Further improve the relevance of search results or context for large language models (LLMs) by incorporating vectors from multiple sources of relevance, such as different source fields (product descriptions, product images, etc.) embedded within the same or different models.

How to get started, and what’s next

Now, with support for the ingestion of scalar quantized vectors, developers can import and work with quantized vectors from their embedding model providers of choice (such as Cohere, Nomic, Jina, Mixedbread, and others)—directly in Atlas Vector Search. Read the documentation and tutorial to get started.

And in the coming weeks, additional vector quantization features will equip developers with a comprehensive toolset for building and optimizing applications with quantized vectors:

Support for ingestion of binary quantized vectors will enable further reduction of storage space, allowing for greater cost savings and giving developers the flexibility to choose the type of quantized vectors that best fits their requirements.

Automatic quantization and rescoring will provide native capabilities for scalar quantization as well as binary quantization with rescoring in Atlas Vector Search, making it easier for developers to take full advantage of vector quantization within the platform.

Tourism Holdings Limited (THL) originally became a MongoDB customer in 2019, using MongoDB Atlas to help manage a wide variety of telematics data.

I was very excited to welcome Charbel Abdo, Solutions Architect for THL at MongoDB .local Sydney in July 2024 to hear more about how the company has significantly expanded its use of MongoDB.

The largest RV rental company in the world, THL has branches in New Zealand (where it is headquartered), Australia, the US, Canada, the UK and Europe. Specializing in building, renting, and selling camper vans, THL has a number of well-known brands under its umbrella.

In recent years, THL has made a number of significant digital transformation and technology stack optimization efforts, moving from a ‘bolt-on’ approach that necessitated the use of a distributed search and analytics engine to an integrated search solution with MongoDB Atlas.

THL operates a complex ecosystem managed by their in-house platform, Motek, which handles booking, pricing, fleet management, and more—with MongoDB Atlas as the central database.

Its +7,000 RVs are fitted with telematics devices that send information—such as location, high-speed events, engine problems, and geofences or restricted areas (for example, during the Australian bushfires of 2020)—to vehicles’ onboard computers.

THL initially used a bolt-on approach for complex search functionalities by extending their deployment footprint to include a stand-alone instance of Elasticsearch.

This setup, while functional, introduced significant data synchronization and performance issues, as well as increased maintenance overhead. Elasticsearch struggled under heavy loads which led to critical failures and system instability, resulting in THL experiencing frequent outages and data inconsistencies.

After two years of coping with these challenges, THL resolved to migrate away from ElasticSearch. After doing due diligence, they identified the MongoDB developer data platform’s integrated Search capabilities as the optimum solution.

"A couple of months later, we had migrated everything," said Abdo. "Kudos to the MongoDB account team. They were exceptional."

The migration process turned out to be relatively straightforward. By iteratively replacing Elasticsearch with MongoDB Atlas Search, THL was able to simplify its architecture, reduce costs, and eliminate the synchronization issues that had plagued the system. The simplification also led to significant performance and reliability improvements.

Because it no longer needed the dedicated sync resources processing millions upon millions of records per day, THL was able to turn off its Elasticsearch cluster and to consolidate its resources.

“All data sync related issues were gone, eliminated. But also we got our Friday afternoons back, which is always a good thing!” added Abdo.

Abdo’s team can now also use existing monitoring tools rather than having to set up something completely separate from the standalone search engine they were using.

“Sometimes, changes are easier than you think,” said Abdo. “We spent two-and-a-half years with our faulty solutions just looking for ways to patch up all the problems that we were having. We tried everything except actually looking into how much it would actually take to migrate. We wasted so much time, so much effort, so much money. While if we had thought about this a couple of years ago, it would have been a breeze.”

“Over-engineering is bad, simple is better,” he noted.

Want to boost your resume quickly? MongoDB is introducing two new Learning Badges, Building gen AI Apps and Deploying and Evaluating gen AI Apps. Unlike high-stakes certifications, which cover a large breadth and depth of subjects, these digital credentials are focused on specific topics, making them easier and quicker to earn. Best of all, they’re free!

The Building Gen AI Applications with MongoDB Learning Badge validates users’ knowledge of developing gen AI applications using MongoDB Atlas Vector Search. It recognizes your understanding of semantic search and how to build chatbots with retrieval-augmented generation (RAG), MongoDB, and Langchain.

The Deploying and Evaluating Gen AI Applications with MongoDB Learning Badge validates users’ knowledge of optimizing the performance and evaluating the results of gen AI applications. It recognizes your understanding of chunking strategies, performance evaluation techniques, and deployment options within MongoDB for both prototyping and production stages.

Learn, prepare, and earn

To earn your badge, simply complete the Learning Badge Path and take a short assessment at the end. Once you pass the short assessment, you'll receive an email with your official Credly badge and digital certificate. You can share it on social media, in email signatures, or on digital resumes. Additionally, you'll gain inclusion in the Credly Talent Directory, where you will be visible to recruiters from top employers and can open up new career opportunities.

Learning paths are like curated roadmaps that guide you through essential concepts and skills needed for the assessment. Each badge has its own learning path:

• Building Gen AI Apps Learning Badge Path: This learning path guides you through the foundations of building a gen AI application with MongoDB Atlas Vector Search. You'll learn what semantic search is and how you can leverage it across a variety of use cases. Then you'll learn how to build your own chatbot by creating a RAG application with MongoDB and Langchain.

• Deploying and Evaluating Gen AI Apps Learning Badge Path: This learning path will help you take a gen AI application from creation to full deployment, with a focus on optimizing performance and evaluating results. You'll explore chunking strategies, performance evaluation techniques, and deployment options in MongoDB for both prototyping and production stages. We recommend completing the Building gen AI Apps Learning Badge Path before beginning this path.

Badge up with MongoDB

MongoDB Learning Badges offer a valuable opportunity to showcase your commitment to continuous learning and expertise in specific topics. These digital credentials not only recognize your educational achievements but also serve as a testament to your knowledge and skills.

Whether you're a seasoned developer, an aspiring data scientist, or an enthusiastic student, earning a MongoDB badge can significantly enhance your profile and open up new opportunities in your field.

Start earning your badges today—it’s quick, effective, and free! Visit MongoDB Learning Badges to begin your journey toward becoming a gen AI application expert and boosting your career prospects.

We’re excited to announce a highly requested feature: Dark mode is now available for MongoDB Documentation!

Every day, developers from all backgrounds—beginners to experts—turn to the MongoDB Documentation. It’s packed with comprehensive resources that help you build modern applications using MongoDB and the Atlas developer data platform.

With detailed information and step-by-step guides, it’s an invaluable tool for improving your skills and making your development work smoother. From troubleshooting tricky queries to exploring new features, MongoDB Documentation is there to support your projects and help you succeed.

With dark mode, you can now switch to a darker interface that’s easier on the eyes. Whether you’re working late or prefer a subdued color palette, dark mode enhances your MongoDB Documentation experience.

How to enable dark mode

Enabling dark mode is simple. Just click on the sun and moon icon at the top right of the page to switch between dark mode, light mode, and system settings. It will initially default to your system settings. This is a personal setting and won't affect other users within the project or organization.

We’ve designed dark mode to provide the same user-friendly experience you’re used to and stay consistent across different tools in the developer workflow, including MongoDB Atlas, which is also available in dark mode.

We're all about making your reading experience top-notch! Dark mode is here because you asked for it through our feedback widget on the Docs page. Whether you’re an early adopter of dark mode or just trying it out, we’d love your opinion. Just drop your feedback in the widget next to the color theme selector on the MongoDB Documentation page.

Less strain, more gain

Dark mode offers a sleek, modern look that brings a refreshing change from the traditional light mode. Beyond its stylish appearance, dark mode also provides significant practical benefits.

Reducing the amount of bright light emitted from your screen helps minimize eye strain and fatigue, making extended periods of device use more comfortable.

For those using OLED screens, dark mode can help conserve battery life, as these screens consume less power by displaying darker pixels.

Whether you’re coding into the late hours or just looking for a more comfortable viewing experience, dark mode is a simple yet powerful tool to enhance your development experience.

Last week I was in London for MongoDB.local London—the 19th stop of the 2024 MongoDB.local tour—where MongoDB, our customers, and our AI partners came together to share solutions we’ve been building that enable companies to accelerate their AI journey. I love attending these events because they offer an opportunity to celebrate our collective achievements, and because it’s great to meet so many (mainly Zoom) friends in person!

One of the highlights of MongoDB.local London 2024 was the release of our reference architecture with our MAAP partners AWS and Anthropic, which supports memory-enhanced AI agents. This architecture is already helping businesses streamline complex processes and develop smarter, more responsive applications.

We also announced a robust set of vector quantization capabilities in MongoDB Atlas Vector Search that will help developers build powerful semantic search and generative AI applications with more scale—and at a lower cost. Now, with support for the ingestion of scalar quantized vectors, you can import and work with quantized vectors from your embedding model providers of choice, including MAAP partners Cohere, Nomic, and others.

A big thank you to all of MongoDB’s AI partners, who continually amaze me with their innovation. MongoDB.local London was another great reminder of the power of collaboration, and I’m excited for what lies ahead as we continue to shape the future of AI together. As the Brits say: Cheers!

Welcoming new AI and tech partners

In September we also welcomed seven new AI and tech partners that offer product integrations with MongoDB. Read on to learn more about each great new partner!

Arize

Arize AI is a platform that helps organizations visualize and debug the flow of data through AI applications by quickly identifying bottlenecks in LLM calls and understanding agentic paths.

"At Arize AI, we are committed to helping AI teams build, evaluate, and troubleshoot cutting-edge agentic systems. Partnering with MongoDB allows us to provide a comprehensive solution for managing the memory and retrieval that these systems rely on”, said Jason Lopatecki, co-founder and CEO of Arize AI. “With MongoDB’s robust vector search and flexible document storage, combined with Arize’s advanced observability and evaluation tools, we’re empowering developers to confidently build and deploy AI applications."

Baseten

Baseten provides the applied AI research and infrastructure needed to serve custom and open-source machine learning models performantly, scalably, and cost-efficiently.

"We're excited to partner with MongoDB to combine their scalable vector database with Baseten's high-performance inference infrastructure and high-performance models. Together, we're enabling companies to build and deploy generative AI applications, such as RAG apps, that not only scale infinitely but also deliver optimal performance per dollar,” said Tuhin Srivastava, CEO of Baseten. “This partnership empowers developers to bring mission-critical AI solutions to market faster, while maintaining cost-effectiveness at every stage of growth."

Doppler

Doppler is a cloud-based platform that helps teams manage, organize, and secure secrets across environments and applications that can be used throughout the entire development lifecycle.

“Doppler rigorously focuses on making the easy path, the most secure path for developers. This is only possible with deep product partnerships with all the tooling developers have come to love. We are excited to join forces with MongoDB to make zero-downtime secrets rotation for non-relational databases effortlessly simple to set up and maintenance-free,” said Brian Vallelunga, founder and CEO of Doppler. “This will immediately bolster the security posture of a company’s most sensitive data without any additional overhead or distractions."

Haize Labs

Haize Labs automates language model stress testing at massive scales to discover and eliminate failure modes. This, alongside their inference-time mitigations and observability tools, enables the risk-free adoption of AI.

"We're thrilled to partner with MongoDB in empowering companies to build RAG applications that are both powerful yet secure, safe, and reliable,” said Leonard Tang, co-founder and CEO of Haize Labs. “MongoDB Atlas has streamlined the process of developing production-ready GenAI systems, and we're excited to work together to accelerate customers' journey to trust and confidence in their GenAI initiatives."

Modal

Modal is a serverless platform for data and AI/ML engineers to run and deploy code in the cloud without having to think about infrastructure. Run generative AI models, large-scale batch jobs, job queues, and more, all faster than ever before.

“The coming wave of intelligent applications will be built on the potent combination of foundation models, large-scale data, and fast search,” explained Charles Frye, AI Engineer at Modal. “MongoDB Atlas provides an excellent platform for storing, querying, and searching data, from hot new techniques like vector indices to old standbys like lexical search. It's the perfect counterpart to Modal's flexible compute, like serverless GPUs. Together, MongoDB and Modal make it easy to get started with this new paradigm, and then they make it easy to scale it out to millions of users querying billions of records & maxing out thousands of GPUs.”

Portkey AI

Portkey AI is an AI gateway and observability suite that helps companies develop, deploy, and manage LLM-based applications.

"Our partnership with MongoDB is a game-changer for organizations looking to operationalize AI at scale. By combining Portkey's LLMOps expertise with MongoDB's comprehensive data solution, we're enabling businesses to deploy, manage, and scale AI applications with unprecedented efficiency and control,” said Ayush Garg, Chief Technology Officer of Portkey AI. “Together, we're not just streamlining the path from POC to production; we're setting a new standard for how businesses can leverage AI to drive innovation and deliver tangible value."

Reka

Reka offers fully multimodal models including images, videos with audio, text, and documents to empower AI agents that can see, hear, and speak.

"At Reka, we know how challenging it can be to retrieve information buried in unstructured multimodal data. We are excited to join forces with MongoDB to help companies test and optimize multimodal RAG features for faster production deployment,” said Dani Yogatama, CEO of Reka. “Our models understand and reason over multimodal data including text, tables, and images in PDF documents or conversations in videos. Our joint solution streamlines the whole RAG development lifecycle, speeding up time to market and helping companies deliver real values to their customers faster."

But wait, there's more!

To learn more about building AI-powered apps with MongoDB, check out our AI Resources Hub, and stop by our Partner Ecosystem Catalog to read about our integrations with MongoDB’s ever-evolving AI partner ecosystem.

Resilience and scalability are critical for today's production applications. MongoDB and Kubernetes are both well known for their ability to support those needs to the highest level. To better enable developers using MongoDB and Kubernetes, we’ve introduced a series of updates and capabilities that makes it easier to manage MongoDB across multiple Kubernetes clusters. In addition to the previously released support for running MongoDB replica sets and Ops Manager across multiple Kubernetes clusters, we're excited to announce the public preview release of support for Sharded Clusters spanning multiple Kubernetes clusters (GA to follow in November 2024).

Support for deployment across multiple Kubernetes clusters is facilitated through the Enterprise Kubernetes Operator. As a recap for anyone unaware, the Enterprise Operator automates the deployment, scaling, and management of MongoDB clusters in Kubernetes. It simplifies database operations by handling tasks such as backups, upgrades, and failover, ensuring consistent performance and reliability in the Kubernetes environment.

Multi-Kubernetes cluster deployment support enhances availability, resilience, and scalability for critical MongoDB workloads, empowering developers to efficiently manage these workloads within Kubernetes. This approach unlocks the highest level of availability and resilience by allowing shards to be located closer to users and applications, increasing geographical flexibility and reducing latency for globally distributed applications.

Deploying replica sets across multiple Kubernetes clusters

MongoDB replica sets are engineered to ensure high availability, data redundancy, and automated failover in database deployments. A replica set consists of multiple MongoDB instances—one primary and several secondary nodes—all maintaining the same dataset. The primary node handles all write operations, while the secondary nodes replicate the data and are available to take over as primary if the original primary node fails. This architecture is critical for maintaining continuous data availability, especially in production environments where downtime can be costly.

Support for deploying MongoDB replica sets across multiple Kubernetes clusters helps ensure this level of availability for MongoDB-based applications running in Kubernetes. Deploying MongoDB replica sets across multiple Kubernetes clusters enables you to distribute your data, not only across nodes in the Kubernetes cluster, but across different clusters and geographic locations, ensuring that the rest of your deployments remain operational (even if one or more Kubernetes clusters or locations fail) and facilitating faster disaster recovery.

To learn more about how to deploy replica sets across multiple Kubernetes clusters using the Enterprise Kubernetes Operator, visit our documentation.

Sharding MongoDB across multiple Kubernetes clusters

While replica sets duplicate data for resilience (and higher read rates), MongoDB sharded clusters divide the data up between shards, each of which is effectively a replica set, providing resilience for each portion of the data. This helps your database handle large datasets and high-throughput operations since each shard has a primary member handling write operations to that portion of the data; this allows MongoDB to scale up the write throughput horizontally, rather than requiring vertical scaling of every member of a replica set. In a Kubernetes environment, these shards can now be deployed across multiple Kubernetes clusters, giving higher resilience in the event of a loss of a Kubernetes cluster or an entire geographic location. This also offers the ability to locate shards in the same region as the applications or users accessing that portion of the data, reducing latency and improving user experience. Sharding is particularly useful for applications with large datasets and those requiring high availability and resilience as they grow.

Support for sharding MongoDB across multiple Kubernetes clusters is currently in public preview and will be generally available in November.

Deploying Ops Manager across multiple Kubernetes clusters

Ops Manager is the self-hosted management platform that supports automation, monitoring, and backup of MongoDB on your own infrastructure. Ops Manager's most critical function is backup, and deploying it across multiple Kubernetes clusters greatly improves resilience and disaster recovery for your MongoDB deployments in Kubernetes.

With Ops Manager distributed across several Kubernetes clusters, you can ensure that backups of deployments remain robust and available, even if one Kubernetes cluster or site fails. Furthermore, it allows Ops Manager to efficiently manage and monitor MongoDB deployments that are themselves distributed across multiple clusters, improving resilience and simplifying scaling and disaster recovery.

To learn more about how to deploy Ops Manager across multiple Kubernetes clusters using the Enterprise Kubernetes Operator, visit our documentation.

5G, IoT, 클라우드 컴퓨팅과 같은 최신 기술의 발전이 가속화되면서 소프트웨어 개발에 대한 민첩성과 확장성, 효율성에 대한 요구가 그 어느 때보다 커지고 있습니다. LG그룹 산하의 한국 대표 모바일 및 인터넷 사업자인 LG유플러스는 모바일, 홈 및 기업서비스를 운영하며, 최근에는 인프라·데이터·플랫폼 중심의 B2B AI 전략으로 고객 성장을 이끄는 AI 사업자로 발돋움하고 있습니다.

LG유플러스 클라우드 플랫폼 개발팀의 진보은 소프트웨어 엔지니어는 MongoDB.local Seoul 2024에서 MongoDB Atlas를 활용한 개발자 플랫폼 혁신 사례를 공유했습니다.

LG유플러스는 최신 서비스를 제공하기 위한 애플리케이션 현대화 과정에서 내부 프로세스 간소화를 위한 노력의 일환으로 Uplus Cloud Management Platform(UCMP)을 구축했습니다. UCMP는 클라우드 환경 및 서비스 전반을 관리하고 보안 위험을 모니터링하며 원활한 서비스 출시에도 활용할 수 있는 LG유플러스의 내부 개발자 플랫폼입니다.

퍼블릭 클라우드 도입 증가에 따라 LG유플러스는 클라우드 서비스에서 발생하는 방대한 양의 데이터를 처리하기 위해 한층 강화된 인프라 보안 기능이 필요했고, 이를 위해 MongoDB의 유연한 도큐먼트 모델로 구동되는 멀티클라우드 개발자 데이터 플랫폼인 MongoDB Atlas를 도입했습니다.

MongoDB Atlas 구축 전 관계형 데이터베이스를 사용하던 LG유플러스는 클라우드 환경에서 사용되는 비정형 데이터의 양과 복잡성을 다룰 수 있는 더 적합한 데이터베이스가 필요했습니다. 가령 데이터 보안 점검 결과를 제공하는 프라울러(Prowler)와 같은 외부 시스템 사용 시 새로운 버전이 출시될 때마다 달라지는 구조와 형식에 맞춰 지속적인 스키마 업데이트가 필요해, 관계형 데이터베이스에서는 상당한 시간을 투입해야 했습니다.

LG유플러스는 인프라 보안을 위해 350개의 AWS 계정에 대한 점검을 진행하며, 계정 당 점검 결과는 취약점 항목별로 최대 2,500건입니다. 그리고 전체 계정에 대한 정기 점검 결과는 3개월 간 저장되는데 이때 다뤄야 하는 점검 결과는 무려 1,000만건에 달합니다.

진보은 엔지니어는 “기존 관계형 데이터베이스로 데이터를 구성했다면 정형화된 스키마로 인해 데이터 점검 결과 형식이 변경될 때마다 스키마 변경 대응에 많은 시간과 노력을 투입했을 것”이라며 “MongoDB의 도큐먼트 모델은 스키마 변경 없이 필요한 데이터를 바로 저장하고 읽을 수 있고, 복잡하고 계층적인 문서 구조를 유연하게 처리할 수 있어 비정형 데이터 처리에 이상적이다. 방대한 양의 데이터를 성능 저하 없이 효과적으로 처리할 수 있는 확장성도 MongoDB를 선택한 이유”라고 전했습니다.

MongoDB Atlas 도입은 LG유플러스의 데이터 쿼리 처리 방식에도 획기적인 변화를 가져왔습니다. 인프라 보안 기능을 위해 실행되는 다양한 밀리세컨드(millisecond) 단위의 실시간 API 관리에 MongoDB의 집계 파이프라인(aggregation pipeline)을 활용해 대규모 데이터 세트를 효율적으로 필터링, 그룹화 및 처리할 수 있었습니다.

예를 들어 취약점별로 보안 점검 결과를 집계하는 쿼리의 경우, 비효율적인 그룹화 및 집계 작업으로 인해 약 87만 건의 데이터를 대상으로 쿼리를 실행하는 데 13초가 소요됐습니다. LG유플러스는 MongoDB의 집계 파이프라인을 통해 집계 방식과의 연관성에 따라 필드를 분리하고 쿼리를 튜닝해 쿼리 실행 시간을 무려 99.11% 단축했습니다.

진보은 엔지니어는 “MongoDB를 처음 사용함에도 불과 3개월만에 UCMP의 인프라 보안 기능을 오픈할 수 있었다. 이렇게 단기간에 MongoDB Atlas를 구축할 수 있었던 것은 MongoDB의 낮은 러닝커브 덕분”이라며 “MongoDB Repository와 Mongo Template을 내부 시스템 환경에 맞춰 커스터마이징해 정교한 쿼리도 빠르게 작성할 수 있다”고 전했습니다.

이어 “무엇보다 간편하게 초기 인프라를 구성할 수 있어 실무에 빠르게 적용할 수 있었다. MongoDB 클러스터에 UCMP의 레플리카 세트를 생성해 고가용성과 안정성을 유지하면서 CPU 사용량에 따라 시스템을 오토스케일(auto-scale) 할 수 있다”고 덧붙였습니다.

앞으로 LG유플러스는 비용 효율화와 쿼리 최적화, 데이터 관리 고도화를 위해 MongoDB Atlas가 제공하는 다양한 기능을 탐색해 나갈 계획입니다.

진보은 엔지니어는 “스케줄링을 통한 비용 효율화를 위해 MongoDB Atlas의 functions 및 triggers 기능을 활용해 급격한 트래픽 증가나 정기적인 배치 작업이 예정된 경우에도 상황에 맞춰 효과적으로 트래픽을 관리할 것이다. 또한 쿼리 성능 개선을 위한 권장사항을 제공하는 Performance Advisor로 쿼리 실행 시간을 모니터링하고 인덱스 사용에 대한 추천을 받아 데이터베이스를 최적화할 수 있을 것으로 기대된다”고 밝혔습니다.

With the rapid advancement of emerging technologies like 5G, IoT, and AI, the demand for agile, scalable, and efficient software development has never been greater.

As Korea’s pioneering mobile and internet provider and a key subsidiary of LG Corporation, LG U+ has built a reputation for excellence in mobile, home, and corporate customer services. Recently, LG U+ expanded its offerings to include AI services, implementing a business-to-business AI strategy focused on infrastructure, data, and platforms to drive customer growth.

At MongoDB.local Seoul 2024, Boeun Jin, a Software Engineer from LG U+’s Cloud Platform Development Team, showcased the company’s innovation journey with MongoDB Atlas.

To streamline internal processes and to modernize its applications, LG U+ developed the Uplus Cloud Management Platform (UCMP). This internal developer platform is designed to manage cloud and service environments, monitor security risks, and ensure smooth service deployment.

As public cloud adoption in South Korea surged, LG U+ recognized the need to enhance its infrastructure’s security capabilities to handle the vast amounts of data processed through its cloud services. To address this, LG U+ moved to MongoDB Atlas, the multi-cloud developer data platform powered by MongoDB's flexible document data model.

Before deploying MongoDB Atlas, LG U+ relied on relational databases (RDBMS). However, as the volume and complexity of the unstructured data in LG U+’s cloud environments grew, the company’s previous RDBMS solution proved increasingly inadequate. For example, external systems like Prowler, used for making data security scans, required constant schema updates to keep pace with the evolving structure and format of new releases, a process that was very time-consuming.

For infrastructure security, LG U+ managed scan results from 350 AWS accounts, each generating up to 2,500 results per vulnerability category. With scan results retained for three months, this resulted in the management of 10 million scans.

“A relational database would struggle with these changes efficiently,” said Boeun Jin. “We would spend considerable time and resources addressing schema updates every time the data format changed, due to the rigid structure of relational databases. In contrast, MongoDB's document model is ideal for managing unstructured data, as it allows for direct storage and retrieval without the need for schema updates. Its flexibility in handling complex and hierarchical document structures, along with its scalability to manage large volumes of data without compromising performance, were key factors in our decision.”

One of the most notable improvements LG U+ noticed after adopting MongoDB Atlas was related to the company’s data query management. The team used MongoDB's aggregation pipeline to handle various millisecond real-time APIs for infrastructure security to efficiently filter, group, and process large data sets.

For example, queries that aggregated security scan results by vulnerability initially took over 13 seconds to process across 870,000 data records, due to inefficient grouping and aggregation operations. With MongoDB Atlas’s aggregation pipeline, LG U+ saw a 99.11% reduction in query execution time. This was achieved by optimizing field separation and tuning queries according to their relevance to the aggregation method.

“Although new to MongoDB, our team successfully launched UCMP's infrastructure security capabilities within just three months. MongoDB’s low learning curve allowed us to customize MongoDB Repository and Mongo Template to our internal system environment and quickly write complex queries,” explained Boeun Jin.

“Most importantly, the ease of configuring the initial infrastructure enabled us to bring it to production swiftly. By creating replica sets of UCMP within the MongoDB cluster, we ensure high availability and reliability, and that the system auto-scales with CPU usage,” she added.

Looking ahead, LG U+ plans to further explore MongoDB Atlas’s features to streamline costs, optimize queries, and enhance data management.

“For cost efficiency, we aim to leverage MongoDB Atlas’ functions and triggers to manage traffic spikes and scheduled batches effectively. In addition, we expect to optimize our database by monitoring query execution time and referring to index usage recommendations from the Performance Advisor, which provides insights to improve query performance,” said Boeun Jin.

카셰어링(car-sharing)이라는 혁신적인 모빌리티 문화를 한국에 정착시킨 쏘카는 차량 공유를 넘어 퍼스널 모빌리티, 주차, 숙박 예약을 아우르는 종합 모빌리티 플랫폼 기업으로 진보하고 있습니다. 이 변화의 중심에는 기술과 데이터를 통해 고객에게 편리한 서비스를 제공하기 위한 쏘카의 지속적인 노력이 있으며, 2023년 5월 선보인 숙박 예약 서비스 ‘쏘카스테이’는 그 대표적인 예입니다.

원활한 예약 경험을 제공하는 것은 결코 간단하지 않았습니다. 사용자가 예약 가능한 숙소를 한 눈에 파악하고 맞춤형 숙소를 추천 받을 수 있는 스마트한 검색 엔진을 구축하기 위해서는 쏘카스테이가 보유한 자체 인벤토리 외에도 타사 숙박 예약 서비스 채널의 데이터를 통합하는 과정이 필요했습니다. 그리고 각기 다른 형식과 데이터 구조를 사용하는 타사 채널의 정보를 매끄럽게 취합하기 위해 이를 통합 관리할 수 있는 데이터베이스가 필요했습니다.

쏘카의 양준영, 차현철 소프트웨어 엔지니어는 MongoDB.local Seoul 2024에서 MongoDB Atlas Search를 통한 쏘카스테이의 빠르고 효율적인 검색 엔진 구축 사례를 소개했습니다.

양준영 엔지니어는 “도큐먼트 데이터베이스 모델의 유연한 스키마를 기반으로 한 MongoDB Atlas Search는 개발자가 사전 정의된 경직된 데이터베이스 구조에 제한 받지 않고 다양한 데이터 형식을 관리할 수 있다는 점에서 효과적이다. 또한 샤딩을 통한 수평 확장을 지원해 쏘카가 보유한 대량의 데이터를 여러 서버에 분산해 효율적으로 처리하면서도 검색 성능을 고속으로 유지할 수 있다”고 설명했습니다.

쏘카 팀이 뽑은 MongoDB Atlas Search의 또 다른 강점은 바로 풍부한 쿼리 기능입니다. MongoDB 데이터베이스 문서에 대한 고급 검색 기능을 제공하는 MongoDB Atlas Search를 통해 팀은 텍스트 검색, 필터링, 랭킹 등 다양한 조건과 연산자를 사용한 복합 쿼리를 작성할 수 있었습니다. 뿐만 아니라 키워드, 화이트스페이스(whitespace)부터 한국어 검색을 위한 노리(nori)까지 다양한 분석기(analyzer)를 지원한다는 점도 효율적이고 사용자 친화적인 검색 환경 구축에 큰 도움이 됐습니다.

차현철 엔지니어는 “쏘카스테이는 MongoDB Atlas Search로 다양한 숙박 서비스 채널이 제공하는 데이터를 통합해, 사용자의 검색조건에 맞춰 필터링된 결과를 제공한다”며 “나아가 지역별 검색 기능을 제공하기 위해 제주, 속초 등 지역 단위로 리전 코드를 정의하고, 스프링 배치(Spring Batch)를 사용해 검색용 데이터베이스를 매일 새벽에 정기적으로 업데이트하며 최신 숙소 정보를 제공하고 있다”고 덧붙였습니다.

프로모션과 같이 특정 이벤트가 적용된 숙소 목록을 필터링하는 기능도 MongoDB의 유연한 검색 아키텍처를 통해 쉽게 구현할 수 있었습니다. 쏘카가 직접 관리하는 이벤트 데이터의 경우, 팀은 간단하게 필드값과 어그리게이션(aggregation) 조건을 추가하는 것만으로도 전체 시스템 업데이트 없이 쉽게 필터링 기능을 제공할 수 있었습니다.

쏘카 팀은 MongoDB Atlas Search 구축 후 검색 기능 뿐만 아니라 성능 측면에서도 획기적인 효과를 경험하고 있습니다. 차현철 엔지니어는 “260MB 인덱스 규모에 달하는 5만 개의 데이터에 대한 검색 성능과 사용자 경험을 모니터링한 결과, 한 달 간 사용자의 약 90%에게 평균 11.6ms, 99%에게는 18.9ms 이내에 응답을 제공하며 놀랍도록 빠른 성능 개선을 이뤘다”고 밝혔습니다.

쏘카스테이는 최근 주요 랜드마크나 여행 동선에 맞춰 숙소를 검색하려는 사용자가 늘어남에 따라 MongoDB Atlas Search의 geoWithin operator를 사용한 새로운 지도 기반 검색 기능도 개발하고 있습니다.

양준영 엔지니어는 “MongoDB Atlas Search 기반으로 구축한 다양한 검색 기능은 향후 쏘카스테이의 비즈니스 성장에 따라 더 많은 혁신을 가능케 할 것”이라며 “애플리케이션에 강력하고 유연한 검색 기능을 통합하고자 하는 개발자라면 MongoDB Atlas Search는 좋은 선택지가 될 수 있다”고 강조했습니다.

활기찬 여행 산업의 중심에 선 야놀자는 디지털 시대로의 도약을 통해 여행의 미래를 혁신하고 있습니다. 지난 2007년 국내 온라인 호텔 예약 서비스로 시작한 야놀자는 현재 전 세계 206개국에서 숙박, F&B, 레저, 광고 솔루션 등 다양한 서비스를 제공하며 글로벌 여행 테크 기업으로 성장했습니다.

야놀자가 오늘날의 스마트한 여행객들에게 최고의 편의성과 혁신적인 서비스를 제공할 수 있는 비결은 바로 데이터베이스 현대화입니다. 최근 야놀자 클라우드기술전략실의 김지환 실장이 MongoDB.local Seoul 행사에 참석해 MongoDB Atlas를 도입한 경험을 공유했습니다.

야놀자는 서비스 확장을 위해 클라우드를 적극 활용하고 있습니다. 일찌감치 퍼블릭 클라우드 환경에 적합한 마이크로서비스 아키텍처를 구축했고, 내부 DB 엔지니어링 조직을 통해 관계형 데이터베이스를 운영 및 관리하고 있습니다.

한편 NoSQL 환경에서는 개발자들이 각자 맡은 서비스에 대한 데이터베이스를 직접 관리하다보니 운영 정책을 표준화하는 데 어려움이 있었고, 전문 엔지니어를 통한 보다 체계적인 NoSQL 통합 운영 관리의 필요성이 절실했습니다. 또한 여러 퍼블릭 클라우드를 사용하는 만큼 무엇보다 클라우드에 친화적인 데이터베이스도 필요했습니다.

김지환 실장은 MongoDB Atlas를 선택한 이유로 “데이터 모델, 확장성, 가용성, 트랜잭션, 인덱스, 어그리게이션(Aggregation), 운영관리, 비용 효과 등 여러 요소를 종합적으로 비교 분석한 결과, MongoDB의 도큐먼트 모델이 복잡한 쿼리와 스키마에 대한 가장 뛰어난 성능을 보여주었고 야놀자의 환경에 가장 적합하다고 판단했다”며 “특히 MongoDB Atlas는 ACID(원자성, 일관성, 고립성, 지속성) 호환 트랙잭션을 실행할 수 있는 우수한 성능과 확장성을 제공할 뿐만 아니라 시계열 데이터, 데이터 파이프 라인, 암호화를 지원하고 타사 대비 강력한 분석 및 모니터링 툴을 보유하고 있다”고 말했습니다.

현재 야놀자는 기존의 NoSQL 데이터베이스를 MongoDB Atlas로 마이그레이션하고 목표로 했던 통합 운영 관리 체계를 구축하고 있으며, 이를 통해 운영 효율성을 높이고 세밀한 모니터링으로 이슈 대응 역량을 강화해 안정적인 대고객 서비스를 제공하고 있습니다.

앞으로 야놀자는 MongoDB와의 협력을 멀티 클라우드 환경으로 확대하며 글로벌 데이터 기업으로 도약할 계획입니다.

김지환 실장은 “현재 AWS, Azure, Google Cloud 등 다양한 클라우드 서비스를 이용해 각 서비스의 성격에 맞게 비즈니스를 발전시키고 있다. 이런 멀티 클라우드 환경에 적합한 유연한 인프라를 구축하는 데 MongoDB의 멀티 클라우드 개발자 데이터 플랫폼이 매우 중요한 역할을 할 것”이라고 밝혔습니다.

Picture this: your field team is miles from the nearest cell tower, but their apps still work flawlessly—tracking assets, updating data, and syncing the moment they're back online. Since even a second of downtime can cost millions, the future of enterprise apps is one where "offline" doesn't mean "out of commission."

Enter MongoDB Atlas and PowerSync, two organizations behind cutting-edge, always-on experiences. The recently announced MongoDB Atlas-PowerSync integration delivers seamless performance and real-time data syncing that keeps businesses running—even when internet connectivity is spotty.

By pairing MongoDB Atlas (the gold standard for cloud databases) with PowerSync (a game-changing sync engine) enterprises can create fully offline-first applications that keep teams productive, no matter where they are. Across a variety of industries— from energy, to manufacturing, to field services, to retail—the new integration delivers smooth performance and reliable bi-directional data sync between backend and frontend systems.

MongoDB Atlas + PowerSync: A power move

The partnership between MongoDB Atlas and PowerSync offers a new level of app performance, especially for those that need to operate seamlessly in both online and offline environments. PowerSync transforms your applications by enabling data synchronization between MongoDB on the backend and SQLite on the front end. This means your apps don’t just cache data while offline—they remain fully operational. Users can perform queries, update records, and sync millions of objects, all while disconnected. As soon as they reconnect, the data syncs effortlessly with MongoDB Atlas, ensuring that no data is lost in the process.

This solution has been refined over a decade, delivering enterprise-grade reliability and scalability. With MongoDB Atlas as the backend, it brings the stability and compliance that enterprises demand. Both PowerSync and MongoDB Atlas are SOC 2 Type 2 audited, ensuring that your data remains secure and compliant with even the strictest regulatory standards. Whether you’re handling a small team or synchronizing millions of users, this combination scales effortlessly, making it a perfect fit for teams of any size.

What’s more, integrating PowerSync into your system doesn’t require a major overhaul. Using MongoDB’s change streams, PowerSync delivers high-performance syncing without the need for configuring complex database triggers. The integration is seamless and designed to minimize disruption, making it a low-stress solution for developers looking for a high-efficiency sync tool.

Finally, PowerSync gives developers complete control over how data is handled. Whether you want to sync immediately or start with a local-first architecture, PowerSync allows for full customization, letting you determine what data is synced and when business logic is applied. No matter your stack or environment, PowerSync is designed to adapt to your specific needs, providing the flexibility and control to create robust, scalable applications.

Where this combo shines: Real-world use cases

Energy & field services: Technicians deep in the field, in low connectivity conditions, but equipped with PowerSync apps. They continue collecting and syncing data, from equipment diagnostics to work orders. Once they reconnect, MongoDB Atlas captures everything in real-time, ensuring no critical data gets lost in the shuffle.

Manufacturing: Whether it’s tracking production metrics or running diagnostics on machinery, PowerSync’s offline-first architecture keeps operations running. Workers can continue making updates and querying data locally, with MongoDB Atlas acting as the central hub for oversight and reporting when the network is back up.

Utilities & Mining: From power grids to remote mining operations, where outages are common, PowerSync delivers secure, bi-directional syncing that ensures your teams have the most up-to-date information, no matter the conditions.

Retail: Network connectivity issues should never prevent app users from completing core retail activities like accepting orders, tracking inventory or making deliveries. Point of sale (POS) apps are typically expected to keep working even when network connectivity is not available.

Unlock your enterprise's potential today

The ability to maintain operations without interruption is increasingly a necessity. By leveraging the powerful integration of MongoDB Atlas and PowerSync, your enterprise can ensure its applications are always ready to perform, regardless of connectivity challenges.

Grab is Southeast Asia’s leading ‘super application,’ offering a wide range of services, targeting both consumers and businesses, including deliveries, mobility, financial services, enterprise and more.

Their range of applications, such as the popular Grab Taxi, Grab Pay, Grab Mart, Grab Ads, and more, count approximately 38 million active users monthly across 500 cities and eight countries.

Managing a high volume of constantly growing users and handling regular spikes in demand and activity means that Grab needs to maintain a robust, scalable, and flexible digital infrastructure.

Presenting at MongoDB.local Singapore in 2024, Grab shared their journey of migrating one of their key service apps—GrabKios—from the Community Edition of MongoDB to MongoDB Atlas. Grab also described how they are expanding their use of MongoDB to support semantic search.

“Transitioning to MongoDB Atlas was not just a migration—it was a strategic move aimed at enhancing our database infrastructure,” said Jude Dulaj Lakshan De Croos, Database Engineering Manager at Grab.

A smooth transition to MongoDB Atlas

Grab’s journey with MongoDB Atlas began with the realization that their existing database infrastructure, while functional, was not equipped to handle the scale and complexity of their operations.

Grab’s eventual migration to MongoDB Atlas was meticulously planned and executed, including extensive testing to ensure a smooth transition.

During the critical testing phase, the creation of a replica “prod clone” environment, allowed Grab to test and refine their migration strategy. This minimized the possibility of unforeseen issues.

The migration also involved the use of Mongomirror. This facilitated the seamless transfer of data from Grab’s self-hosted clusters to MongoDB Atlas.

“We were able to ensure that migration was actually smooth and ran without any issues,” said Swarit Arora, Senior Database Engineer at Grab.

MongoDB Atlas’s developer data platform offers Grab high levels of flexibility and scalability, accommodating Grab’s fast growth (the company recorded a 23% revenue growth YoY in 2024) in an ever-changing digital landscape. MongoDB Atlas also delivers unique automation and streamlining capabilities, as well as enterprise-grade support which led to improved process and database management efficiency.

Efficiency gains with greater scalability, flexibility, performance

MongoDB Atlas provided Grab with an automated, scalable, and secure platform, which empowered its engineering teams to focus on product development rather than database maintenance.

“With MongoDB Atlas, we don’t have to worry about the scaling changes. And with hands-on security we can deliver secure and fast applications,” said Arora. “Being able to configure the exact resources required and then scale up and down based on our requirements is a plus. Considering we don't have to manage the scalability part, this is, I think, saving us around 50% of the time.”

Furthermore, MongoDB Atlas delivers proactive recommendations to Grab’s team. For example, MongoDB Atlas’s Performance Advisor saves the team time by delivering real-time insights and recommendations to optimize query performance, ultimately reducing manual management tasks and increasing database efficiency.

“It is now easy to set up our MongoDB clusters compared to what we were doing when we self-hosted, which was more time-consuming,” added Arora. “Secondly, if we are required to upgrade the cluster version, it is as easy as the click of a button.”

Dedicated analytics nodes mean that Grab’s team is able to enhance the analytical capabilities of any application running on MongoDB.

The successful migration to MongoDB Atlas has positioned Grab to explore new possibilities, including leveraging MongoDB’s advanced features for use cases such as semantic search and AI applications.

Delays are a significant challenge for the airline industry. They disrupt travel plans, erode customer loyalty, and inflict significant financial losses. In an industry built on precision and punctuality, even minor setbacks can have cascading effects. Whether due to adverse weather conditions or unforeseen technical issues, these delays ripple through flight schedules, affecting both passengers and operations managers. While neither group is typically at fault, the ability to quickly reallocate resources and return to normal operations is crucial.

To mitigate these disruptions and restore passenger trust, airlines must have the tools and strategies to quickly identify delays and efficiently reallocate resources. This blog explores how a unified platform with real-time data analysis can be a game-changer in this regard especially in saving costs.

The high cost of delays

Delays from disruptions, like weather events or crew unavailability, pose major challenges for the airline industry. These delays have significant financial impact according to some studies, costing European airlines on average €4,320 per hour per flight. They also create operational challenges like crew disruptions and reduced airplane availability, leading to further delays, which is known in the industry as delay propagation.

To address these challenges, airlines have traditionally focused on optimizing their pre-flight planning processes. However, while planning is crucial, effective recovery strategies are equally essential for minimizing the impact of disruptions. Unfortunately, many airlines have underinvested in recovery systems, leaving them ill-prepared to respond to unexpected events. The consequences of this imbalance include:

• Delay propagation: Initial delays can cascade, causing widespread schedule disruptions.

• Financial and operational damage: Increased costs and inefficiencies strain airline resources.

• Customer dissatisfaction: Poor disruption management leads to negative passenger experiences.

The power of real-time data analysis

In response to the significant challenges posed by flight delays, a real-time unified platform offers a powerful solution designed to enhance how airlines manage disruptions.

Event-driven architectural approach

The diagram below showcases an event-driven architecture that can be used to build a robust and decoupled platform that supports real-time data flow between microservices. In an event-driven architecture, services or components communicate by producing and consuming events, which is why this architecture relies on Pub/Sub (messaging middleware) to manage data flows.

Moreover, MongoDB’s flexible document model and ability to handle high volumes of data make it ideal for event-driven systems. Combining these features with PubSub’s, this approach proves to offer a powerful solution for modern applications that require scalability, flexibility, and real-time processing.

In this architecture, the blue line in the diagram shows the operational data flow. The data simulation is triggered by the application’s front-end and is initialized in the FastAPI microservice. The microservice, in turn, starts publishing airplane sensor data to the custom Pub/Sub topics, which forwards these data to the rest of the architecture components, such as cloud functions, for data transformation and processing.

The data is processed in each microservice, including the creation of analytical data, as shown by the green lines in the diagram. Afterward, data is introduced in MongoDB and fetched from the application to provide the user with organized, up-to-date information regarding each flight.

This leads to more precise and detailed analysis of real-time data for flight operations managers. As a result, new and improved opportunities for resource reallocation can be explored, helping to minimize delays and reduce associated costs for the company.

Microservice overview

As mentioned earlier, the primary goal is to create an event-driven, decoupled architecture founded on MongoDB and Google Cloud services integrations. The following components contribute to this:

• FastAPI: Serves as the main data source, generating data for analytical insights, predictions, and simulation.

• Telemetry data: Pulls and transforms operational data published in the PubSub topic in real-time, storing it in a MongoDB time series collection for aggregation and optimization.

• Application data: Subscribed to a different PubSub topic, this service acknowledges static operational data, including initial route, recalculated route, and disruption status. Therefore, this service will only be triggered provided any of the previous fields are altered. Finally, this data is updated in its MongoDB collection accordingly.

• Vertex AI integration—analytical data flow: A cloud function triggered by PubSub messages that executes data transformations and forwards data to the Vertex AI deployed machine learning (ML) model. Predictions are then stored in MongoDB.

MongoDB: A flexible, scalable, and real-time data solution

Building a unified real-time platform for the airline industry requires efficient management of massive, diverse datasets. From aircraft sensor data to flight cost calculations, data processing and management are central to operations. To meet these demands, the platform needs a flexible data platform capable of handling multiple data types and integrating with various systems. This enables airlines to extract valuable insights from their data and develop features that improve operations and the passenger experience.

Real-time data processing is a must-have feature. This allows airlines to receive immediate alerts about delays, minimizing disruptions and ensuring smooth operations. In fast-paced airport environments, where every minute counts, real-time data processing is indispensable.

For example, integrating MongoDB with Google Cloud's Vertex AI allows for the real-time processing and storage of airplane sensor data, transforming it into actionable insights.

Business benefits

This solution provides real-time access to critical flight data, enabling efficient cost management and operational planning. Immediate access to this information allows flight operation managers to plan ahead, reallocate existing resources, or even initiate recovery procedures in order to diminish the consequences of the identified delay.

Moreover, its ML model customization ensures adaptability to various use cases.

Regarding the platform’s long-term sustainability, it has been purposely designed to integrate highly reliable and scalable products in order to excel in three key standards:

Scalability

• The platform’s compatibility with both horizontal and vertical scaling is clearly demonstrated by its integral design.

• The decoupled architecture illustrates how this solution is divided into different components—and therefore instances—that work together as a cohesive whole.

• Vertical scalability can be achieved by simply increasing the computing power allocated to the designed Vertex AI model, if needed.

Availability

• The decoupled architecture exemplifies the central importance of availability in any project’s design.

• Using different tracks to introduce operational and analytical data into the database allows us to handle any issues in a way that remains unnoticeable to end users.

Latency

• Optimizing the connections between components and integrations within the product is key to achieving the desired results.

• Using PubSub as our asynchronous messaging service helps minimize unnecessary delays and avoid holding resources needlessly.

Get started!

To sum up, this blog has explored how MongoDB can be integrated into an airline flight management system, offering significant benefits in terms of cost savings and enhanced customer experience.

MongoDB Queryable Encryption is a groundbreaking, industry-first innovation developed by the MongoDB Cryptography Research Group that allows customers to encrypt sensitive application data, store it securely in an encrypted state in the MongoDB database, and perform equality and range queries directly on the encrypted data—with no cryptography expertise required. Adding range query support to Queryable Encryption significantly enhances data retrieval capabilities by enabling more flexible and powerful searches. Queryable Encryption is available in MongoDB Atlas, Enterprise Advanced, and Community Edition.

Encryption: Protecting data through every stage of its lifecycle

Encryption is a critical security method for ensuring protection of sensitive data and compliance with regulations like GDPR, CCPA, and HIPAA. It involves rendering data unreadable to anyone without the decryption key. It can protect data in three ways: in-transit (over networks), at-rest (when stored), and in-use (during processing). While encryption in-transit and at-rest are standard for all databases and are well-supported by MongoDB, encryption in-use presents a unique challenge.

Encryption in-use is difficult because encrypted data is unreadable—it looks like random characters and symbols. Traditionally, the database can’t run queries on encrypted data without decrypting it first to make it readable. However, if the database doesn’t have a decryption key, it has to send encrypted data back to the application or system (i.e., the client) that has the key so it can be decrypted before querying. This is a pattern that doesn’t scale well for real-world applications.

This puts organizations in a difficult spot: in-use encryption is important for data privacy and regulatory compliance, but it's hard to implement. In the past, companies have either chosen not to encrypt sensitive data in-use or have employed less secure workarounds that complicate their operations.

MongoDB Queryable Encryption: Safeguarding data in use without sacrificing efficiency

MongoDB Queryable Encryption solves this problem. It allows organizations to encrypt their sensitive data, like personally identifiable information (PII) or protected health information (PHI), and to run equality and range queries directly on that data without having to decrypt it.

Queryable Encryption was developed by the MongoDB Cryptography Research Group, drawing on their pioneering expertise in cryptography and encrypted search, and Queryable Encryption has been peer-reviewed by leading cryptography experts worldwide. Unmatched in the industry, MongoDB is the only data platform that allows customers to run expressive queries directly on non-deterministically encrypted data. This represents a groundbreaking advantage for customers, allowing them to maintain robust protection for their sensitive data without sacrificing operational efficiency or developer productivity by still enabling expressive queries to be performed on it.

Organizations of all sizes, across all industries, can benefit from the impactful outcomes enabled by Queryable Encryption, such as:

• Stronger data protection: Data stays encrypted at every stage—whether in-transit, at-rest, or in-use—reducing the risk of sensitive data exposure or breaches.
• Enhanced regulatory compliance: Provides customers with the necessary tools to comply with data protection regulations like GDPR, CCPA, and HIPAA by ensuring robust encryption at every stage.
• Streamlined operations: Simplifies the encryption process without needing costly custom solutions, specialized cryptography teams, or complex third-party tools.
• Solidified separation of duties: Supports stricter access controls, where MongoDB and even a customer's database administrators (DBAs) don’t have access to sensitive data.

Use cases for Queryable Encryption

MongoDB Queryable Encryption has many use cases for organizations that host sensitive data, regardless of their size or industry. The recent addition of range query support to Queryable Encryption broadens those use cases even wider. Here are some examples to help illustrate how Queryable Encryption could be used to protect and query sensitive data:

• Financial Services
• Credit Scoring: Assess creditworthiness by querying encrypted data such as credit scores and income levels. For example, segment your customers based on credit scores between 600 and 750.
• Fraud Detection: Detect anomalies by querying encrypted transaction amounts for values that exceed typical spending patterns, such as transactions above $10,000.
• Insurance
• Risk Assessment: Personalize policy offerings by querying encrypted client data for risk levels within specified ranges, enhancing customer service without exposing sensitive information.
• Claims Processing: Automate claims processing by querying encrypted claims data for amounts within specific ranges or for claims within time periods, streamlining operations while safeguarding information.
• Healthcare
• Medical Research: Execute range-based searches on encrypted medical records, such as querying encrypted datasets for patients within specific age ranges or for abnormal lab results for medical research.
• Billing and Insurance Processing: Perform secure range queries on encrypted billing data to process insurance claims and payments while protecting patient financial details.
• Education
• Grading Systems: Process encrypted student scores to award grades within specific ranges, ensuring compliance with FERPA while protecting student privacy and maintaining data security.
• Financial Aid Distribution: Analyze encrypted income data within certain ranges to determine eligibility for scholarships and financial aid.

Comprehensive data protection at every stage

With Queryable Encryption, MongoDB offers unmatched protection for sensitive data throughout its entire lifecycle—whether in-transit, at-rest, or in-use. Now, with the addition of range query support, Queryable Encryption meets even more of the demands of modern applications, unlocking new use cases. To get started, explore the Queryable Encryption documentation.

MongoDB is excited to announce enhancements to our LlamaIndex integration. By combining MongoDB’s robust database capabilities with LlamaIndex’s innovative framework for context-augmented large language models (LLMs), the enhanced MongoDB-LlamaIndex integration unlocks new possibilities for generative AI development.

Specifically, it supports vector (powered by Atlas Vector Search), full-text (powered by Atlas Search), and hybrid search, enabling developers to blend precise keyword matching with semantic search for more context-aware applications, depending on their use case.

Building AI applications with LlamaIndex

LlamaIndex is one of the world’s leading AI frameworks for building with LLMs. It streamlines the integration of external data sources, allowing developers to combine LLMs with relevant context from various data formats. This makes it ideal for building application features like retrieval-augmented generation (RAG), where accurate, contextual information is critical. LlamaIndex empowers developers to build smarter, more responsive AI systems while reducing the complexities involved in data handling and query management.

Advantages of building with LlamaIndex include:

• Simplified data ingestion with connectors that integrate structured databases, unstructured files, and external APIs, removing the need for manual processing or format conversion.

• Organizing data into structured indexes or graphs, significantly enhancing query efficiency and accuracy, especially when working with large or complex datasets.

• An advanced retrieval interface that responds to natural language prompts with contextually enhanced data, improving accuracy in tasks like question-answering, summarization, or data retrieval.

• Customizable APIs that cater to all skill levels—high-level APIs enable quick data ingestion and querying for beginners, while lower-level APIs offer advanced users full control over connectors and query engines for more complex needs.

MongoDB's LlamaIndex integration

Developers are able to build powerful AI applications using LlamaIndex as a foundational AI framework alongside MongoDB Atlas as the long term memory database. With MongoDB’s developer-friendly document model and powerful vector search capabilities within MongoDB Atlas, developers can easily store and search vector embeddings for building RAG applications. And because of MongoDB’s low-latency transactional persistence capabilities, developers can do a lot more with MongoDB integration in LlamIndex to build AI applications in an enterprise-grade manner.

LlamaIndex's flexible architecture supports customizable storage components, allowing developers to leverage MongoDB Atlas as a powerful vector store and a key-value store. By using Atlas's Vector Search capabilities, developers can:

• Store and retrieve vector embeddings efficiently (llama-index-vector-stores-mongodb)

• Persist ingested documents (llama-index-storage-docstore-mongodb)

• Maintain index metadata (llama-index-storage-index-store-mongodb)

• Store Key-value pairs (llama-index-storage-kvstore-mongodb)

Adding hybrid and full-text search support

Developers may use different approaches to search for different use cases. Full-text search retrieves documents by matching exact keywords or linguistic variations, making it efficient for quickly locating specific terms within large datasets, such as in legal document review where exact wording is critical. Vector search, on the other hand, finds content that is ‘semantically’ similar, even if it does not contain the same keywords. Hybrid search combines full-text search with vector search to identify both exact matches and semantically similar content. This approach is particularly valuable in advanced retrieval systems or AI-powered search engines, enabling results that are both precise and aligned with the needs of the end-user.

It is super simple for developers to try out powerful retrieval capabilities on their data and improve the accuracy of their AI applications with this integration. In the LlamaIndex integration, the MongoDBAtlasVectorSearch class is used for vector search. All you have to do is enable full-text search, using VectorStoreQueryMode.TEXT_SEARCH in the same class. Similarly, to use Hybrid search, enable VectorStoreQueryMode.HYBRID. To learn more, check out the GitHub repository.

With the MongoDB-LlamaIndex integration’s support, developers no longer need to navigate the intricacies of Reciprocal Rank Fusion implementation or to determine the optimal way to combine vector and text searches—we’ve taken care of the complexities for you. The integration also includes sensible defaults and robust support, ensuring that building advanced search capabilities into AI applications is easier than ever. This means that MongoDB handles the intricacies of storing and querying your vectorized data, so you can focus on building!

AI adoption rates are sky-high and showing no signs of slowing down. One of the driving forces behind this explosive growth is the increasing popularity of low- and no-code development tools that make this transformative technology more accessible to tech novices. Buzzy, an AI-powered no-code platform that aims to revolutionize how applications are created, is one such company. Buzzy enables anyone to transform an idea into a fully functional, scalable web or mobile application in minutes.

Buzzy developers use the platform for a wide range of use cases, from a stock portfolio tracker to an AI t-shirt store. The only way the platform could support such diverse applications is by being built upon a uniquely versatile data architecture. So it’s no surprise that the company chose MongoDB Atlas as its underlying database.

Creating the buzz

Buzzy’s mission is simple but powerful: to democratize the creation of applications by making the process accessible to everyone, regardless of technical expertise.

Founder Adam Ginsburg—a self-described husband, father, surfer, geek, and serial entrepreneur—spent years building solutions for other businesses. After building and selling an application that eventually became the IBM Web Content Manager, he created a platform allowing anyone to build custom applications quickly and easily. Buzzy initially focused on white-label technology for B2B applications, which global vendors brought to market. Over time, the platform evolved into something much bigger.

The traditional method of developing software, as Ginsburg puts it, is dead. Ginsburg observed two major trends that contributed to this shift: the rise of artificial intelligence (AI) and the design-centric approach to product development exemplified by tools like Figma. Buzzy set out to address two major problems. First, traditional software development is often slow and costly. Small-to-medium-sized business (SMB) projects can take anywhere from $50,000 to $250,000 and nine months to complete. Due to these high costs and lengthy timelines, many projects either fail to start or run out of resources before they’re finished.

The second issue is that while AI has revolutionized many aspects of development, it isn’t a cure-all for generating vast amounts of code. Generating tens of thousands of lines of code using AI is not only unreliable but also lacks the security and robustness that enterprise applications demand. Additionally, the code generated by AI often can’t be maintained or supported effectively by IT teams. This is where Buzzy found a way to harness AI effectively, using it in a co-pilot mode to create maintainable, scalable applications.

Buzzy’s original vision was focused on improving communication and collaboration through custom applications. Over time, the platform’s mission shifted toward no-code development, recognizing that these custom apps were key drivers of collaboration and business effectiveness.

Initially, Buzzy's offerings were somewhat rudimentary, producing functional but unpolished B2B apps. However, the platform soon evolved. Instead of building their own user experience (UX) and user interface (UI) capabilities, Buzzy integrated with Figma, giving users access to the design-centric workflow they were already familiar with. The advent of large language models (LLMs) provided another boost to the platform, enabling Buzzy to accelerate AI-powered development.

What sets Buzzy apart is its unique approach to building applications. Unlike traditional development, where code and application logic are often intertwined, Buzzy separates the "app definition" from the "core code." This distinction allows for significant benefits, including scalability, maintainability, and better integration with AI. Instead of handing massive chunks of code to an AI system—which can result in errors and inefficiencies—Buzzy gives the AI a concise, consumable description of the application, making it easier to work with. Meanwhile, the core code, written and maintained by humans, remains robust, secure, and high-performing. This approach not only simplifies AI integration but also ensures that updates made to Buzzy’s core code benefit all customers simultaneously, an efficiency that few traditional development teams can achieve.

Flexible platform, fruitful partnership

The partnership between Buzzy and MongoDB has been crucial to Buzzy’s success. MongoDB’s Atlas developer data platform provides a scalable, cost-effective solution that supports Buzzy’s technical needs across various applications. One of the standout features of MongoDB Atlas is its flexibility and scalability, which allows Buzzy to customize schemas to suit the diverse range of applications the platform supports. Additionally, MongoDB’s support—particularly with new features like Atlas Vector Search—has allowed Buzzy to grow and adapt without complicating its architecture.

In terms of technology, Buzzy’s stack is built for flexibility and performance. The platform uses Kubernetes and Docker running on Node.js with MongoDB as the database. Native clients are powered by React Native, using SQLLite and Websockets for communication with the server. On the AI side, Buzzy leverages several models, with OpenAI as the primary engine for fine-tuning its AI capabilities.

Thanks to the MongoDB for Startups program, Buzzy has received critical support, including Atlas credits, consulting, and technical guidance, helping the startup continue to grow and scale. With the continued support of MongoDB and an innovative approach to no-code development, Buzzy is well-positioned to remain at the forefront of the AI-driven application development revolution.

A Buzzy future

Buzzy embodies the spirit of innovation in its own software development lifecycle (SDLC). The company is about to release two game-changing features that are going to take AI driven App development to the next level: Buzzy FlexiBuild, which will allow users to build more complex applications using just AI prompts, and Buzzy Automarkup, which will allow Figma users to easily mark up screens, views, lists, forms, and actions with AI in minutes.

For insurance companies, determining the right technology investments can be difficult, especially in today's climate where technology options are abundant but their future is uncertain. As is the case with many large insurers, there is a need to consolidate complex and overlapping technology portfolios. At the same time, insurers want to make strategic, future-proof investments to maximize their IT expenditures.

What does the future hold, however? Enter scenario planning. Using the art of scenario planning, we can find some constants in a sea of uncertain variables, and we can more wisely steer the organization when it comes to technology choices. Consider the following scenarios:

• Regulatory disruption: A sudden regulatory change forces re-evaluation of an entire market or offering.

• Market disruption: Vendor and industry alliances and partnerships create disruption and opportunity.

• Tech disruption: A new CTO directs a shift in the organization's cloud and AI investments, aligning with a revised business strategy.

What if you knew that one of these three scenarios was going to play itself out in your company but weren’t sure which one? How would you invest now to prepare for one of the three?

At the same time that insurers are grappling with technology choices, they’re also facing clashing priorities:

• Running the enterprise: supporting business imperatives and maintaining health and security of systems.

• Innovating with AI: maintaining a competitive position by investing in AI technologies.

• Optimizing spend: minimizing technology sprawl, technical debt, and maximizing business outcomes.

Data modernization

What is the common thread among all these plausible future scenarios? How can insurers apply scenario planning principles while bringing diverging forces into alignment? There is one constant in each scenario, and that’s the organization’s data—if it’s hard to work with, any future scenario will be burdened by this fact.

One of the most critical strategic investments an organization can make is to ensure data is easy to work with. Today, we refer to this as data modernization, which involves removing the friction that manifests itself in data processing, ensuring data is current, secure, and adaptable. For developers, who are closest to the data, this means enabling them with a seamless and fully integrated developer data platform along with a flexible data model.

In the past, data models and databases would remain unchanged for long periods. Today, this approach is outdated. Consolidation creates a data model problem, resulting in a portfolio with relational, hierarchical, and file-based data models—or, worst of all, a combination of all three. Add to this the increased complexity that comes with relational models, including supertype-subtype conditional joins and numerous data objects, and you can see how organizations wind up with a patchwork of data models and overly complicated data architecture.

A document database, like MongoDB Atlas, stores data in documents and is often referred to as a non-relational (or NoSQL) database. The document model offers a variety of advantages and specifically excels in data consolidation and agility:

• Serves as the superset of all other data model types (relational, hierarchical, file-based, etc.)

• Consolidates data assets into elegant single-views, capable of accommodating any data structure, format, or source

• Supports agile development, allowing for quick incorporation of new and existing data

• Eliminates the lengthy change cycles associated with rigid, single-schema relational approaches

• Makes data easier to work with, promoting faster application development

By adopting the document model, insurers can streamline their data operations, making their technology investments more efficient and future-proof.

The challenges of making data easier to work with include data quality. One significant hurdle insurers continue to face is the lack of a unified view of customers, products, and suppliers across various applications and regions. Data is often scattered across multiple systems and sources, leading to discrepancies and fragmented information. Even with centralized data, inconsistencies may persist, hindering the creation of a single, reliable record. For insurers to drive better reporting, analytics, and AI, there's a need for a shared data source that is accurate, complete, and up-to-date. Centralized data is not enough; it must be managed, reconciled, standardized, cleansed, and enriched to maintain its integrity for decision-making. Mastering data management across countless applications and sources is complex and time-consuming. Success in master data management (MDM) requires business commitment and a suite of tools for data profiling, quality, and integration. Aligning these tools with business use cases is essential to extract the full value from MDM solutions, although the process can be lengthy.

Informatica’s MDM solution and MongoDB

Informatica’s MDM solution has been developed to answer the key questions organizations face when working with their customer data:

• “How do I get a 360-degree view of my customer, partner and & supplier data?”

• “How do I make sure that my data is of the highest quality?”

The Informatica MDM platform helps ensure that organizations around the world can confidently use their data and make business decisions based on it. Informatica’s entire MDM solution is built on MongoDB Atlas, including its AI engine, Claire.

Informatica MDM solves the following challenges:

• Consolidates data from overlapping and conflicting data sources.

• Identifies data quality issues and cleanses data.

• Provides governance and traceability of data to ensure transparency and trust.

Insurance companies typically have several claim systems that they’ve amassed over the years through acquisitions, with each one containing customer data. The ability to relate that data together and ensure it’s of the highest quality enables insurers to overcome data challenges.

MDM capabilities are essential for insurers who want to make informed decisions based on accurate and complete data. Below are some of the different use cases for MDM:

• Modernize legacy systems and processes (e.g. claims or underwriting) by effectively collecting, storing, organizing, and maintaining critical data

• Improve data security and improve fraud detection and prevention

• Effective customer data management for omni-channel engagement and cross- or up-sell

• Data management for compliance, avoiding or predicting in advance any possible regulatory issues

Taking the next step

For insurance companies navigating the complexities of modern technology and data management, MDM combined with powerful tools like MongoDB and Informatica provide a strategic advantage. As insurers face an uncertain future with potential regulatory, market, and technological disruptions, investing in a robust data infrastructure becomes essential. MDM ensures that insurers can consolidate and cleanse their data, enabling accurate, trustworthy insights for decision-making.

By embracing data modernization and the flexibility of document databases like MongoDB, insurers can future-proof their operations, streamline their technology portfolios, and remain agile in an ever-changing landscape. Informatica’s MDM solution, underpinned by MongoDB Atlas, offers the tools needed to master data across disparate systems, ensuring high-quality, integrated data that drives better reporting, analytics, and AI capabilities.

Gamuda Berhad is a leading Malaysian engineering and construction company with operations across the world, including in Australia, Taiwan, Singapore, Vietnam, the United Kingdom, and more. The company is known for its innovative approach to construction through the use of cutting-edge technology.

Speaking at MongoDB.local Kuala Lumpur in August 2024, John Lim, Chief Digital Officer at Gamuda said: “In the construction industry, AI is increasingly being used to analyze vast amounts of data, from sensor readings on construction equipment to environmental data that impacts project timelines.”

One of Gamuda’s priorities is determining how AI and other tools can impact the company’s methods for building large projects across the world. For that, the Gamuda team needed the right infrastructure, with a database equipped to handle the demands of modern AI-driven applications.

MongoDB Atlas fulfilled all the requirements and enabled Gamuda to deliver on its AI-driven goals.

Why Gamuda chose MongoDB Atlas

“Before MongoDB, we were dealing with a lot of different databases and we were struggling to do even simple things such as full-text search,” said Lim.

“How can we have a tool that's developer-friendly, helps us scale across the world, and at the same time helps us to build really cool AI use cases, where we're not thinking about the infrastructure or worrying too much about how things work but are able to just focus on the use case?”

After some initial conversations with MongoDB, Lim’s team saw that MongoDB Atlas could help it streamline its technology stack, which was becoming very complex and time consuming to manage.

MongoDB Atlas provided the optimal balance between ease of use and powerful functionality, enabling the company to focus on innovation rather than database administration.

“I think the advantage that we see is really the speed to market. We are able to build something quickly. We are fast to meet the requirements to push something out,” said Lim.

Chi Keen Tan, Senior Software Engineer at Gamuda, added, “The team was able to use a lot of developer tools like MongoDB Compass, and we were quite amazed by what we can do. This [ability to search the items within the database easily] is just something that’s missing from other technologies.”

Being able to operate MongoDB on Google Cloud was also a key selling point for Gamuda: “We were able to start on MongoDB without any friction of having to deal with a lot of contractual problems and billing and setting all of that up,” said Lim.

How MongoDB is powering more AI use cases

Gamuda uses MongoDB Atlas and functionalities such as Atlas Search and Vector Search to bring a number of AI use cases to life.

This includes work implemented on Gamuda’s Bot Unify platform, which Gamuda built in-house using MongoDB Atlas as the database. By using documents stored in SharePoint and other systems, this platform helps users write tenders quicker, find out about employee benefits more easily, or discover ways to improve design briefs.

“It’s quite incredible. We have about 87 different bots now that people across the company have developed,” Lim said.

Additionally, the team has developed Gamuda Digital Operating System (GDOS), which can optimize various aspects of construction, such as predictive maintenance, resource allocation, and quality control.

MongoDB’s ability to handle large volumes of data in real-time is crucial for these applications, enabling Gamuda to make data-driven decisions that improve efficiency and reduce costs.

Specifically, MongoDB Atlas Vector Search enables Gamuda’s AI models to quickly and accurately retrieve relevant data, improving the speed and accuracy of decision-making. It also helps the Gamuda team find patterns and correlations in the data that might otherwise go unnoticed.

Gamuda’s journey with MongoDB Atlas is just beginning as the company continues to explore new ways to integrate technology into its operations and expand to other markets.

Interesting ideas are bound to emerge when great minds come together, so there was no shortage of interesting ideas on October 2nd, when MongoDB’s Developer Relations team hosted our second-ever AI Build Together event at MongoDB.local London.

In some ways, the event is similar to a hackathon: a group of developers come together to solve a problem. But in other ways, the event is quite different. While hackathons normally take an entire day and involve intensive coding, the AI Build Together events are organized to take place over just a few hours and don't involve any coding at all. Instead, it’s all based around discussion and ideation. For these reasons, MongoDB’s Developer Relations team likes to dub them “think-a-thons.”

Our first AI Build Together event was held earlier this year at .local NYC. After seeing the energy in the room and the excitement from attendees, our Developer Relations team knew it wanted to host another one.

The .local London event’s fifty attendees—which included developers from numerous industries and leading AI innovators who served as mentors—came together to brainstorm and discuss AI-based solutions to common industry problems.

The AI mentors included: Loghman Zadeh (gravity9), Ben Gutkovich (Superlinked), Jesse Martin (Hasura), Marlene Mhangami (Microsoft), Igor Alekseev (AWS), and John Willis and Patrick Debois (co-founders of DevOps).

Upon arrival, participants joined a workflow group best aligned with their industry and/or area of interest—AI for Education, AI for DevOps, AI for Healthcare, AI for Optimizing Travel, AI for Supply Chain, and AI for Productivity.

The discussions were lively, and it was amazing to see how much energy these attendees brought to their discussions.

For example, the AI for Education workflow group vigorously discussed developing a personalized AI education coach to help students develop their educational plans and support them with career advice.

Meanwhile, the AI for Healthcare workflow group focused on the idea of creating an AI drive tool to provide personalized healthcare to patients and real-time insights to their providers.

The AI for Productivity team came up with a clever product that helps you read, digest, and identify the key aspects of long legal documents.

A talented artist was also brought in to visualize each workflow group’s problem statements and potential solutions—literally and figuratively illustrating their innovative ideas.

All in all, our second time hosting this event was deemed a success by everyone involved.

“It was impressive to see how attendees, regardless of their technical background, found ways to contribute to complex AI solutions,” says Loghman Zadeh, AI Director at gravity9, who served as one of the event’s advisors. “Engaging with so many creative and forward-thinking individuals, all eager to push the boundaries of AI innovation was refreshing. The collaborative atmosphere fostered dynamic discussions and allowed participants to explore new ideas in a supportive environment.”

If you’re interested in taking part in events like these—which offer a range of networking opportunities—there are three more MongoDB.local events slated for 2024—Sao Paulo, Paris, and Stockholm. Additionally, you can join your local MongoDB user group to learn from and connect with other MongoDB developers in your area.

Roughly 20% of the US population is neurodiverse, which means that you likely work with a colleague who learns and navigates the workplace (and the world) differently than you do. Which is a good thing! Studies have shown that hiring neurodiverse individuals benefits workplaces, with Deloitte noting that organizations “can gain a competitive edge from increased diversity in skills, ways of thinking, and approaches to problem-solving.”

Config at MongoDB—which Cian and I are the global leaders of—recognizes the prevalence, importance, and power of neurodiversity in the workplace. Config’s mission is to educate both our members and the wider employee population at MongoDB about neurodiversity in the workplace, and through education to empower them to embrace—and champion—neurodiversity.

Since it was founded in April 2023, Config’s membership has grown by over 150%, and it now has members in New York, Dublin, Paris, Gurugram, and Sydney. In fact, more than 200 people who span a range of MongoDB teams—from Engineering and Product, to the People team, to Marketing—take part in Config.

We like to say that no one succeeds until all of us succeed. And that no one belongs until all of us belong. As managers, culture leaders, and as people, it's our responsibility to do whatever we can to make that true. Invisible differences like neurodiversity are hard to spot, but they enrich our work and our lives. Config.MDB plays an important role in helping us achieve this ambition.

Making an impact on the MongoDB community

Over the last year and a half, Config has held over fifteen events globally—with almost 1,000 employees in attendance. Config has held educational events for both the group’s members and the wider MongoDB audience on neurodiversity-related topics like autism awareness and ADHD awareness, along with events tailored to allies and members who identify as neurodivergent or who are part of a neurodivergent family.

Config has also held training sessions for MongoDB people managers that provide them knowledge and tools to better manage neurodiverse team members. Ger Hartnett, an Engineering Lead at MongoDB said the training “gave me a much better understanding and appreciation for neurodiversity. This course was truly eye-opening for me. I learned practical ways to be more inclusive and supportive, both at work and in everyday life.”

The group also holds quarterly virtual meetings to share the latest updates, personal experiences, and practical tips for members, focusing on career development, benefit entitlements, and events happening within MongoDB.

Outside of events and training sessions, Config has had a broader business impact on the company, with some Config leads partnering with the employee inclusion and recruiting teams to put together an interview accommodation program. This program supports candidates who are neurodiverse or have a disability by allowing them to apply for special requests to make their interview experience more inclusive and enjoyable.

Making a difference for individual members

Config’s focus on educational and training events has had a dramatic and direct impact on members. The group is a safe space for neurodiverse or disabled people to share their experiences and seek advice on various issues. Cian is one of Config’s founding members, and had this to say about his personal experience:

I was diagnosed with dyslexia in college and wanted to start a group like Config after speaking with other employees who were neurodiverse. We agreed that there was a need for a group like this at MongoDB. After the group was formed, I attended several events that focused on ADHD and saw a lot of similarities between traits and experiences of those with ADHD and myself. After attending these events, struggles that I had and that I thought were personality traits could be a sign of ADHD, I turned to some of our members for guidance on how to seek a diagnosis. Earlier this year, I was diagnosed with ADHD by a medical professional. I have noticed an improvement in my quality of life, and thanks to Config, I have a lot of valuable tips and resources to help me in my day-to-day. Had it not been for Config and these events I would still be none the wiser.

Config has also made an impact on employees who are parents of neurodivergent children, like Sarah Lin, a senior information/content architect and Config member:

I joined Config to be part of the change I want to see in the world—to help make the inclusive and supportive workplace I'd want my autistic daughter to experience. I certainly hope I'm contributing because membership has benefitted me personally. I've learned more about different types of neurodivergence and ways to support my colleagues. From our employee resource group events, I've learned more about autism and the lives of autistic adults so that I can be a better support for my daughter as we look toward her adulthood. The best part has been conversations with other parents and seeing myself reflected in their struggles, persistence, and achievements.

Looking ahead

As Config continues to expand its footprint within MongoDB, the group plans to introduce advanced educational programming to raise awareness for neurodiversity in the workplace. It also plans to hold workshops to foster professional development and executive functioning. Config also hopes to grow its global membership to provide community outreach at scale for nonprofit organizations that specifically service neurodiverse individuals.

Ultimately, Config’s aim is to create the best environment for teams at MongoDB. Our view of success is not only the “what” but also the “how.” Being sustainable, encouraging growth through learning, and accomplishing goals as a team are all meaningful to us.

And we believe strongly in the power of allyship; we want MongoDB to be a place where amazing people feel supported and are given the opportunity to do their best. After all, many of us are already close to neurodivergent individuals. One of Config’s Executive Sponsors, Mick Graham, has a daughter who is neurodivergent—which he says gives him extra inspiration to support Config now and in the future.

Overall, being part of Config has raised our understanding of how neurodivergent people navigate the world. And the group—and the inspirations and experiences members have shared—contribute to making MongoDB a place that great people want to be.

As enterprises continue to scale, the need for powerful, seamless data migration tools becomes increasingly important. Adiom, founded by industry veterans with deep expertise in data mobility and distributed systems, is addressing this challenge head-on with its open-source tool, dsync. By focusing on high-stakes, production-level migrations, Adiom has developed a solution that works effortlessly with MongoDB Atlas and makes large-scale migrations to it from NoSQL databases faster, safer, and more predictable.

The real migration struggles

Enterprises often approach migrations with apprehension, and for good reason. When handling massive datasets powering mission-critical services or user-facing applications, even small mistakes can have significant consequences. Adiom understands these challenges deeply, particularly when migrating to MongoDB Atlas. Here are a few of the common pain points that enterprises face:

• Time-consuming processes: Moving large datasets involves extensive planning, testing, and iteration. What’s more, enterprises need migrations that are repeatable and can handle the same dataset efficiently multiple times—something traditional tools often struggle to provide.

• Risk management: From data integrity issues to downtime during the migration window, the stakes are high. Tools that worked for smaller datasets and in lower-tier environments no longer meet the requirements. Custom migration scripts often introduce unforeseen risks, while other databases come with their own unique limitations.

• Cost overruns: Enterprises frequently encounter hidden migration costs—whether it's the need to provision special infrastructure, reworking application code for compatibility with migration plans, or paying SaaS vendors by the row. These complications can balloon the overall migration budget or send the project into the approval death spiral.

To make things even more complicated, the pains feed into each other. The longer the project takes, the more risks need to be accounted for, the longer the planning and testing, and the bigger the cost.

Adiom’s dsync: Power and simplicity in one tool

Dsync was built with these challenges in mind. Designed specifically for large production workloads, dsync enables enterprises to handle complex migrations more easily, lowering the hurdles that typically slow down the process, reducing risks and uncertainty.

Here’s why dsync stands out:

• Ease of deployment: Starting with dsync is incredibly simple. All it takes is downloading a single binary—there’s no need for specialized infrastructure, and it runs seamlessly on VMs or Docker. Users can monitor migrations through the command line or a web interface, giving flexibility depending on the team’s preferences.

• Resilience and Safety: dsync is not only efficient, but it’s also resumable. Should a migration be interrupted, there’s no need to start over. This means that migrations can continue smoothly from where they left off, reducing the risk of downtime and minimizing the complexity of the process.

• Verification: dsync is designed to protect the integrity of migrated data. Dsync features embedded data verification mechanisms that automatically check for consistency between the source and destination databases after migration.

• Security: dsync doesn't store data, doesn't send it outside the organization other than to the designated destination, and supports network encryption.

• No hidden costs: As an open-source tool, dsync eliminates the need to onboard expensive SaaS solutions or purchase licenses in the early stages of the process. It operates independently of third-party vendors, giving enterprises flexibility and control over their migrations without the additional financial burden.

Enhancing MongoDB customers' experiences

For MongoDB customers, the ability to migrate data quickly and efficiently can be the key to unlocking new products, features, and cost savings. With dsync, Adiom provides a solution that can accelerate migrations, reduce risks, and enable enterprises to leverage MongoDB Atlas without the usual headaches.

• Faster time-to-market: By significantly accelerating migrations, dsync allows companies to take advantage of MongoDB Atlas offerings and integrations sooner, offering a direct path to quicker returns on investment.

• Self-service and support: Many migrations can be handled entirely in-house, thanks to dsync’s intuitive design. However, for organizations that need additional guidance, Adiom offers support and has partnered with MongoDB Professional Services and PeerIslands to offer comprehensive coverage during the migration process.

Five compelling advantages of migrating to MongoDB

• Flexible schema: MongoDB’s schema-less design reduces development time by up to 30% by allowing you to change data structures.

• Scalability: You can scale MongoDB to multiple petabytes of data seamlessly using sharding.

• High performance: MongoDB helps to improve read and write speeds by up to 50% compared to traditional databases.

• Expressive Query API: Its advanced querying capabilities reduce query writing time and increase execution efficiency by 70%.

• Partner Ecosystem: MongoDB’s strong partner ecosystem helps with service integrations, AI capabilities, purpose-built solutions, and other significant competitive differentiators.

Conclusion

Dsync is more than just a migration tool—it’s a powerful engine that abstracts away the complexity of managing large datasets across different systems. By seamlessly tying together initial data copying, change-data-capture, and all the nuances of large-scale migrations, dsync lets enterprises focus on building their future, not on the logistics of data transfer. For those interested in technical details, some of those logistics and nuances can be found in our CEO’s blog.

With Adiom and dsync, enterprises no longer have to choose between performance, correctness, or ease of use when planning a migration from another NoSQL database. Dsync provides an enterprise-grade solution that helps to enable faster, more secure, and more reliable migrations. By partnering with MongoDB, Adiom supports you in continuing to innovate without being held back by the limitations of legacy databases.

人工智能（AI) 世界正在以闪电般的速度发展，各种应用层出不穷，其中包括目前最为炫酷的新AI聊天机器人之一：角色AI。角色AI可以进行有趣的对话，帮助学习一门新语言，或者创建用户自己的聊天机器人。

YoMio.AI是一家专注角色AI的天使轮初创公司，聚焦AI娱乐，致力于从各方面让AI成为人类的陪伴。YoMio.AI目前主要开发了AI原生娱乐产品Rubii，并围绕Rubii构建了一整套产品矩阵，将Rubii中的功能解构，创造一套独立的服务，其中包括：全球最快的语音生成推理引擎之一；从Rubii上一键将角色放到其他社交平台，例如QQ；提供公开竞技场测评大语言模型的角色扮演能力（Roleplay LLM Arena）；快速定制富知识机器人等。

初创公司，尤其是AI初创公司正在以最大限度的想象力在改变着我们每天的生活。他们每天在为我们创造工具，而在这个过程中，AI初创公司也迫切需要好用的工具。YoMio.AI创始人Junity指出，就开发而言，初创公司首先最需要的是统一有效的云架构解决方案，将全部应用迁移到一家云；其次，初创公司需求变化快，需要随时更改表单，非关系型数据库更为适配；此外，多语言全文搜索也是一项必要功能。

为了应对以上挑战与需求，MongoDB Atlas成为了YoMio.AI近乎完美的适配解决方案。

利用二进制存储缓存张量，实现MongoDB版Prompt Cache，打造全球最快TTS推理引擎之一。

利用MongoDB储存二进制文件的能力，YoMio.AI实现了行业首个GPT-SoVITS极速推理，成功将原版3秒左右一条音频优化到15秒推理出160条音频（注：GPT-SoVITS是一款先进的TTS框架，在Github上超过30000星标，以跨语言、3秒语音无需训练即可克隆而著称）。据Junity介绍，通过MongoDB Atlas，YoMio.AI无需像PostgreSQL装插件来实现中文全文搜索，也无需像Elastic Search专门配置搜索节点，配置Atlas Index后，仅需简单的代码即可搜索。

Search Index实现多语言全文搜索。

MongoDB 的全文索引可以帮助用户快速地查找包含特定关键字或短语在内的数据。这对很多应用程序来说非常重要，因为可以使用全文索引来快速查找相关数据。在MongoDB支持之下，YoMio.AI不但实现了中日英韩粤多语言搜索，而且能够实现跨语言搜索，甚至是在同一句话中进行混读。

Atlas Vector Search搭配Infinity推理引擎，实现极低延迟且超高性能检索重排。

MongoDB Atlas 提供非常丰富的开箱即用功能，向量检索构建了最低延迟且同时满足检索+重排的系统，并且搭建本地Infinity镜像实现embedding+reranker即插即用，单次检索全流程延迟低于50ms。

除此之外，通过Atlas全球集群(Global Cluster)，YoMio.AI上述系统在全球任何范围内都是低延迟高可用，而实现这一切仅用了两个月。

Junity 解释到，YoMio.AI业务分为ToC和ToB两类。ToC为主推的AI角色Rubii，利用丰富的数据和精进的算法，Rubii正在变得更富场景感和体验感；ToB主推富有定制知识的聊天机器人，YoMio.AI内部检索引擎会将客户的文档分块，转换成向量，并且用知识图谱解析，每一次和机器人对话时，机器人都会获得最符合该对话场景下的文档分片。

无论是ToC端还是ToB端，YoMio.AI都在与时代赛跑，始终要拿出最快、最优质的产品。作为YoMio.AI的数据库技术合作伙伴，MongoDB在AI前沿探索方面也开足马力，正在积极探索AI在应用程序现代化改造中的应用，尤其在代码分析、智能模式映射和代码转换等领域。通过引入AI，MongoDB将进一步简化应用现代化的过程，缩短迁移时间，使企业能够更快地适应市场需求。

随着MongoDB的新发布和革新，YoMio.AI的“闪电式发展”值得期待。

Welcome to the latest article in our “Away From the Keyboard” series, which features interviews with people at MongoDB, discussing what they do, how they prioritize time away from their work, and their advice for others looking to create a more holistic approach to coding.

Rafa Liou, Senior Partner Marketing Manager at MongoDB, was gracious enough to tell us why he's not ashamed to advocate strongly for a healthy work-life balance and how his past career in the wild world of advertising helped him first recognize the need to do so

Q: What do you do at MongoDB?

RAFA: I’m a Marketing Manager focused on MongoDB’s AI partner ecosystem. I help promote our partnerships with companies such as Anthropic, Cohere, LangChain, Together AI, and many others. I work to drive mutual awareness, credibility, and product adoption in the gen AI space via marketing programs. Basically telling the world why we’re better together. It’s a cool job where I’m able to wear many hats and interact with lots of different teams internally and externally.

Q: What does work-life balance look like for you?

RAFA: Work-life balance is really important to me. It’s actually one of the things I value the most in a job. I know some people advise against this but anytime I’m interviewing with a company I ask about it because it definitely impacts my mental health, how I spend my time outside of work, and my ability to do the things I love. I’m very fortunate to work for a company that understands that, and trusts me to do my job and, at the same time, be able to step out for a walk, a workout, not miss a dinner reservation with my husband, or whatever it is. It makes a lot of difference in both my productivity and happiness.

After I log off, you can find me taking a HIIT class, exploring the restaurant scene in LA, or biking at the beach. It’s so good to be able to do all of that stress-free!

Q: How do you ensure you set boundaries between work and personal life?

RAFA: I usually joke that if you do everything you’re tasked with at the pace you’d like things to get done, you will never stop working. It is really important to prioritize them based on value, urgency, and feasibility. By assessing your pipeline more critically, you will be able to distill what needs to be done right now and also be at peace with the things that will be handled down the road, making it easier to disconnect when you’re done for the day. It’s also important to set expectations and boundaries with your manager and teams so you can fully enjoy life after work without worrying about that Slack message when you’re at the movies.

Q: Has work/life balance always been a priority for you, or did you develop it later in your career?

RAFA: Before tech, I worked in advertising, which is a very fast-paced industry with the craziest deadlines. For some time in my career, working relentlessly was not only required, but it was also rewarded by agency culture. When you’re young, nights in the office brainstorming over pizza with friends may sound fun. But it starts to wear you out pretty quickly, especially when you don’t have the time, energy, or even the mental state to enjoy your personal life after long hours. As I matured and climbed a few steps in my career, I felt the urge and empowerment to set some boundaries to protect myself. Now, it’s a non-negotiable factor for me.

Q: What benefits has this balance given you in your career?

RAFA: By constantly exercising prioritization, I’ve become a more efficient professional. When you focus on what really matters, you are also able to execute at higher quality, without distractions or the feeling of getting overwhelmed. Of course, with prioritization comes a lot of trade-offs and discussions with stakeholders on what should be prioritized today versus tomorrow. So, I think I’ve also gotten better at negotiation and conflict resolution (things I’ve always struggled with). Last but not least: having consistent downtime to unwind makes me more creative and energized to come up with new ideas and take on new projects.

Q: What advice would you give to someone seeking to find a better balance?

RAFA: First and foremost: don’t be ashamed of wanting a better work-life balance. I often find people living and breathing work just because they don’t want to be seen as lazy or uncommitted. Once you understand that a better work-life balance will actually make you a better professional—more intentional, efficient, and even strategic (as you will spend energy to solve what creates more value in a timely manner)—it will be easier to have this mindset, communicate it to others, and live by it.

Something more practical would be to start a list of all the things you have to do, acknowledge you can’t finish them all by the end of the day (or week, or month), and ask yourself: Do they all carry the same importance? How can I prioritize them? What would happen if I work on X now instead of Y? I would experiment with this approach and check how you feel and how it impacts your day-to-day life. You might be surprised by the result. Making time for personal life events, hobbies, and meet-ups with family and friends will also help you have something to look forward to after closing your laptop.

This is all easier said than done but I guarantee that once this becomes part of your core values and you find the balance that works for you, it is totally worth it!

Thank you to Rafa Liou for sharing his insights! And thanks to all of you for reading.

For past articles in this series, check out our interviews with:

• Senior AI Developer Advocate, Apoorva Joshi

• Developer Advocate Anaiya Raisinghani

Aktivo Labs, a pioneering health-tech startup based in Singapore, has made significant strides in the fight against chronic diseases. Aktivo Labs develops innovative preventative healthcare technology solutions that encourage healthier lifestyles.

The Aktivo Score® —the flagship product of Aktivo Labs built on MongoDB Atlas—is a simple yet powerful tool designed to guide users toward healthier living.

“By collecting and analyzing data from smartphones and wearables—including physical activity, sleep patterns, and sedentary behavior—the Aktivo Score provides personalized recommendations to help users improve their health,” said Aktivo Labs CTO Jonnie Avinash at MongoDB.local Singapore in August 2024.

Aktivo Labs also works closely with insurance companies. Acting as a data processor, it helps insurers integrate some of the Aktivo Score features into their own apps to improve customer engagement.

Empowering insurers with out-of-the-box apps and user journeys

From the start, the Aktivo Labs engineering team chose to work on MongoDB Atlas because the platform’s document model and cloud nature provided the flexibility and scalability required to support the company’s business model.

The first goal of the engineering team was to enable insurance providers to integrate Aktivo Score smoothly within their own infrastructures.

The team built software development kits (SDKs) that insurers can embed in various iOS and Android apps. The SDKs enable progressive web app journeys for user experience, which insurers can then rebrand and customize as their own.

Next, the Aktivo Labs team created a web portal to help companies manage their apps and monitor their performance. This required discreet direct integrations with a myriad of wearables.

“When we started to deploy things with companies, we were able to replicate this architecture so we could support all kinds of configurations,” Avinash said. “We could give you dedicated clusters if the number of users that you’re expecting is big enough. If you’re not expecting too many customers, we could give you colocated or shared environments.”

Finding more efficiencies, flexibility, and scalability with MongoDB Atlas

“When we started off, one of our challenges was that we had a very small engineering team. A lot of the focus had to be on functionality, and the cost of tech had to be kept low,” said Avinash.

Working on MongoDB Atlas allowed the Aktivo Labs team to focus on product development rather than on database management and overhead costs.

As the company grew and expanded to markets across Asia, Africa, and the Middle East, another challenge arose: Aktivo Labs needed to ensure its platform could scale and handle large volumes of disparate data efficiently.

MongoDB Atlas was the optimal solution because its fully managed multi-cloud platform could easily scale as the company grew. MongoDB Atlas also provided Aktivo Labs the flexibility it needed to handle the wide variety, volume, and complexity of data generated by users’ health metrics.

Based on insights from the MongoDB Atlas oplog, the engineering team made proactive updates to the database in real-time in anticipation of dynamic changes to leaderboards and challenges in the app. This approach enables Aktivo Labs to manage complex data flows efficiently, ensuring that users always have access to the latest metrics about their health.

MongoDB Atlas’s secondary nodes and analytics nodes provide isolated environments for intensive data processing tasks, such as calculating risk scores for diabetes and hypertension. This separation ensures that the primary user-facing applications remain responsive, even during periods of heavy data processing.

These isolated environments have also been an important factor in achieving compliance with the data-anonymization requirements from health insurers.

“The moment you start showing that it’s a managed service and you’re able to show a lot of these things, the amount of faith that both auditors and clients have in us is a lot more,” said Avinash.

Powered by MongoDB Atlas, Aktivo Labs is now looking to expand into U.S. and European markets, pursuing its mission of preventing chronic diseases on a global scale.

MongoDB Atlas was designed with elasticity at its core and has always allowed customers to scale capacity vertically and horizontally, as required and automatically. Today, these inherent capabilities are even better and more cost-effective. At the recent MongoDB.local London, MongoDB announced several new MongoDB Atlas features that improve elasticity and help optimize costs while maintaining the performance and availability that business-critical applications demand. These include scaling each shard independently, extending storage beyond 4 TB or more, and 5X more responsive auto-scaling.

Organizations and their customers are inherently dynamic, with operations, web traffic, and application usage growing unpredictably and non-linearly. For example, website traffic can spike due to a single video going viral on social media, and holidays are a frequent cause of application usage slowdowns.

Traditionally, organizations have tackled this volatility by over-provisioning infrastructure, often at significant cost.

Cloud adoption has improved the speed at which infrastructure can be provisioned in response to growing and volatile demand. Simultaneously, companies are focused on striking the perfect balance between performance and cost efficiency. This balance is acute in the current economic climate, where cost optimization is a top priority for Infrastructure & IT Operations (I&O) leaders.

However, scaling infrastructure to meet demand without overprovisioning can be complex and costly. Organizations have often relied on manual processes (like scheduled scripts) or dedicated teams (like IT ops) to manage this challenge. MongoDB Atlas enables a more effective approach. With MongoDB Atlas, customers can manage flexible provisioning, zero-downtime scaling, and easy auto-scaling of their clusters. From October 2024, all Atlas customers with dedicated tier clusters can employ these recently announced enhancements for improved cost optimization.

Granular resource provisioning

MongoDB’s tens of thousands of customers have complex and diverse workloads with constantly changing requirements. Over time, workloads can grow unpredictably, requiring scaling up storage, compute, and IOPS independently and at differing granularities. Imagine a global retailer preparing for Cyber Monday, when traffic could be 512% higher than average— additional resources to serve customers are vital.

Independent shard scaling enables customers running MongoDB Atlas to do this in a cost-optimal manner. Customers can independently scale the tier of individual shards in a cluster when one or more shards experience disproportionately higher traffic. For customers running workloads on sharded clusters, scaling each shard independently of all other shards is now an option (for example, only the shards serving US traffic during Thanksgiving). Customers can scale operational and analytical nodes independently in a single shard.

This improves scalability and cost-optimization by providing fine-grained control to add resources to hot shards while maintaining the resources provisioned to other shards. All Atlas customers running dedicated clusters can use this feature through Terraform and the Admin API. Support for independent shard auto-scaling and configuration management via the Admin API and Terraform will be available in late 2024.

Extended Storage and IOPS in Azure: MongoDB is introducing the ability to provision additional storage and IOPS on Atlas clusters running on Azure. This enables support for optimal performance without over-provisioning. Customers can create new clusters on Azure to provision additional IOPS and extended storage with 4TB or more on larger clusters (M40+). This feature is being rolled out and will be available to all Atlas clusters by late 2024. Head over to our docs page to learn more.

With these updates, customers have greater flexibility and granularity in provisioning and scaling resources across their Atlas clusters on all three major cloud providers. Therefore, customers can optimize for performance and costs more effectively.

More responsive auto-scaling

Granular provisioning is excellent for optimizing costs while ensuring availability for an expected increase in traffic. However, what happens if a website gets 13X higher traffic or a surge in app interactions due to an unexpected social media post?

Several enhancements to the algorithms and infrastructure powering MongoDB’s auto-scaling capabilities were announced in October 2024 at .local London. Cumulatively, these improve the time taken to scale and the responsiveness of MongoDB’s auto-scaling engine. Customers running dynamic workloads, particularly those with sharper peaks, will see up to 5X improvement in responsiveness. Smarter scaling decisions by Atlas will ensure that resource provisioning is optimized while maintaining high performance. This capability is available on all Atlas clusters with auto-scaling turned on, and customers should experience the benefits immediately.

Industry-leading MongoDB Atlas customers like Conrad and Current use auto-scaling to automatically scale their compute capacity, storage capacity, or both without needing custom scripts, manual intervention, or third-party consulting services. Customers can set upper and lower tier limits, and Atlas will automatically scale their storage and tiers depending on their workload demands. This ensures clusters always have the optimal resources to maintain performance while optimizing costs. Take a look at how Coinbase is optimizing for both availability and cost in the volatile world of cryptocurrency with MongoDB Atlas’ help, or read our auto-scaling docs page to learn more.

Optimize price and performance with MongoDB Atlas

As businesses focus more on optimizing cloud infrastructure costs, the latest MongoDB Atlas enhancements— independent shard scaling, more responsive auto-scaling, and extended storage with IOPS—empower organizations to manage resources efficiently while maintaining top performance. These tools provide the flexibility and control needed to achieve cost-effective scalability.

Estamos muito satisfeitos em anunciar que o MongoDB 8.0, a versão mais recente do banco de banco de dados de documento mais popular do mundo, usado por milhões de desenvolvedores e mais de 50.000 clientes em todo o mundo, já está disponível para o público em geral. O MongoDB 8.0 baseia-se nos recursos principais do setor do MongoDB para fornecer melhorias significativas de desempenho, custos reduzidos e maior facilidade de uso, de implantações locais a aplicativos distribuídos globalmente em escala empresarial.

Os desenvolvedores sempre gostaram de criar com o MongoDB, por isso garantimos que a versão 8.0 mantivesse o padrão extremamente alto para a usabilidade do desenvolvedor. O MongoDB 8.0 também foi criado para exceder os requisitos mais rigorosos de segurança, resiliência, disponibilidade e desempenho dos nossos clientes e é a versão mais impactante do MongoDB até agora. O MongoDB 8.0 oferece aos clientes a base mais sólida possível para a criação de uma gama ampla de aplicativos, para o presente e para o futuro.

Jim Scharf, Chief Technology Officer, MongoDB

Para o MongoDB 8.0, concentramos nossos esforço de engenharia em torno de quatro objetivos principais:

• Otimize o desempenho para a mais ampla variedade de aplicativos

• Ofereça criptografia avançada para desbloquear novos casos de uso

• Reduza os custos e aumente a escala com o dimensionamento horizontal rápido e intuitivo para alta disponibilidade

• Garanta a resiliência para demanda inesperada de aplicação

Então, como essas metas realmente beneficiam as equipes à medida que criam e gerenciam aplicativos? Começaremos analisando por que você deve usar o MongoDB 8.0.

Se você tem experiência com o MongoDB ou está apenas começando a usar banco de dados, o MongoDB 8.0 é uma ótima base para novos aplicativos e para impulsionar os aplicativos existentes. A versão 8.0 combina tudo que os desenvolvedores mais gostam do MongoDB, como uma experiência de desenvolvimento intuitiva e consistente, suporte para um conjunto amplo de casos de uso e facilidade de uso operacional, com melhorias de desempenho incomparáveis.

Principais razões para mudar para o MongoDB 8.0

1. O MongoDB 8.0 está 30% mais rápido do que antes

À medida que a geração e o uso dos aplicativos de dados aumentam, pequenas ineficiências podem levar a aumentos desproporcionais nos custos de infraestrutura. Como muitos clientes interagem com as empresas principalmente por meio de seus aplicativos, um desempenho ruim ou inconsistente dos aplicativos pode levar à insatisfação do cliente, à perda de oportunidades e à redução da receita. Portanto, é fundamental que as organizações garantam que seus aplicativos tenham um desempenho consistentemente bom.

O MongoDB 8.0 melhora o desempenho consideravelmente ao possibilitar que os aplicativos consultem e transformem dados de forma rápida e eficiente, com uma melhora da taxa de transferência de até 36%. As otimizações de arquitetura no MongoDB 8.0 reduziram o uso de memória e os tempos de query, e uma combinação de processamento e otimizações em lote mais eficientes tornou possível gravações em massa 56% mais rápidas e gravações simultâneas 20% mais rápidas durante a replicação de dados. Além disso, as otimizações no MongoDB 8.0 significam que o banco de dados pode processar volumes maiores de dados de série temporal e executar operações 200% mais rápidas, com o uso de recursos e custos reduzidos.

2. O MongoDB 8.0 está mais seguro do que nunca

A proteção e a segurança de dados são essenciais. Com o aumento da complexidade e do volume de dados transmitidos, armazenados e processados em vários ambientes, proteger informações confidenciais com criptografia robusta é mais importante do que nunca. As organizações devem proteger seus dados durante todo o seu ciclo de vida — em trânsito pelas redes, em repouso onde estão armazenados e enquanto estão em uso para consulta e processamento. No entanto, pode ser um desafio criptografar os dados enquanto eles são consultados e processados, deixando-os vulneráveis à exposição ou exfiltração por agentes mal-intencionados.

A Queryable Encryption do MongoDB é uma novidade do setor, desenvolvida pelo MongoDB Criptografia Research Group. Ele permite que os clientes criptografem dados confidenciais no lado do cliente, os armazenem com segurança como dados criptografados totalmente aleatórios no banco de dados MongoDB e executem consultas expressivas nos dados criptografados para processamento.

O MongoDB 8.0 agora inclui suporte para queries de intervalo — além das queries de igualdade — para expandir a recuperação segura de dados com maior flexibilidade para pesquisas comuns. Com o Queryable Encryption, os dados necessários permanecem criptografados até chegarem a um usuário final autorizado usando uma chave de descriptografia controlada pelo cliente, sem a necessidade de experiência em criptografia.

3. O MongoDB 8.0 torna mais barato e fácil de escalar

À medida que as organizações crescem, os requisitos de seus aplicativos tendem a desenvolver-se. Por exemplo, o dimensionamento para oferecer suporte a milhões de usuários pode ser um desafio para as organizações que originalmente projetaram seus aplicativos para milhares de usuários. Isso ocorre porque a implementação de alterações arquitetônicas em aplicativos de produção pode envolver um esforço significativo que pode ser caro e demorado.

Com o MongoDB 8.0, o dimensionamento horizontal agora é mais rápido e fácil, e a um custo menor. Com o dimensionamento horizontal, os aplicativos podem ser dimensionados além dos limites dos recursos tradicionais de banco de dados, dividindo os dados em vários servidores, conhecidos como shards, sem a necessidade de provisionar previamente quantidades cada vez maiores de recursos de computação para um único servidor. Os novos recursos de fragmentação do MongoDB 8.0 distribuem dados entre fragmentos até 50 vezes mais rápido e a um custo até 50% menor para começar.

4. O MongoDB 8.0 oferece mais controle para ajudar seus aplicativos a serem executados sem problemas

Os usuários finais esperam experiências consistentes com aplicação , mesmo durante períodos de alta demanda e picos de uso. As organizações que não possuem um banco de dados operacional altamente durável correm o risco de ter experiências ruins para os clientes, com atrasos no comportamento dos aplicativos (ou até mesmo tempo de inatividade) durante os períodos de alta demanda.

O MongoDB 8.0 oferece maior controle para equipes que otimizam o desempenho do banco de dados durante picos imprevisíveis de uso e períodos prolongados de alta demanda. O MongoDB 8.0 conta com novas funcionalidades para definir um limite de tempo máximo padrão para processar queries, rejeitar tipos recorrentes de queries problemáticas e definir configurações de query para permanecer durante eventos como reinicializações do banco de dados. Essas funcionalidades ajudam a proporcionar um comportamento consistente dos aplicativos e alto desempenho, independentemente de picos de demanda ou eventos inesperados.

Pronto para experimentar o MongoDB 8.0?

Se estiver criando um novo aplicativo, a maneira mais fácil de começar a usar o MongoDB 8.0 é acessando mongodb.com/try, onde você pode se cadastrar para uma conta gratuita no Atlas, baixar a edição Community e saber mais sobre o gerenciamento do MongoDB com uma assinatura Enterprise Advanced.

Se você estiver executando uma versão anterior do MongoDB, há tutoriais de atualização úteis para o MongoDB Atlas e sistemas autogerenciados. Além disso, a documentação e a ajuda especializada da equipe de serviços profissionais do MongoDB estão disponíveis.

Se você tiver um aplicação existente que não esteja usando o MongoDB como banco de banco de dados , confira a ferramenta Relational Migrator do MongoDB . O Relational Migrator pode ajudá-lo a mapear esquemas relacionais existentes para um esquema MongoDB , realizar migrações de dados e converter queries, triggers e procedimentos armazenados relacionais existentes para trabalhar com o MongoDB.

As equipes de engenharia e de produtos do MongoDB ouviram cuidadosamente os comentários dos desenvolvedores, e o MongoDB 8.0 foi criado levando em conta a usabilidade do desenvolvedor — bem como a segurança, a durabilidade, a disponibilidade e o desempenho — em primeiro lugar. Estamos ansiosos para que você experimente e temos certeza de que aproveitará os ganhos de desempenho e outros benefícios do MongoDB 8.0!

我们很高兴地宣布，MongoDB 最新版本 8.0 现已全面上市。MongoDB 是一款广受全球欢迎的文档数据库，数百万名开发者和 50,000 多名客户都在使用这一数据库。MongoDB 8.0 以 MongoDB 行业领先的各项功能为基础，可显著提升性能，降低成本，增强易用性，适用于从本地部署到企业级全球分布式应用程序构建等各种使用场景。

许多开发者长期以来一直青睐使用 MongoDB 构建应用程序，因此我们确保 8.0 版将开发者可用性保持在极高水平。MongoDB 8.0 的构建还超越了客户最严格的安全性、韧性、可用性和性能要求，是 MongoDB 迄今为止表现最为出色的版本。MongoDB 8.0 为客户现在和将来构建各种应用程序提供了最坚实的基础。

Jim Scharf, Chief Technology Officer, MongoDB

在开发 MongoDB 8.0 时，我们的工程团队专注于四项关键目标：

• 优化各种应用程序的性能

• 引入创新的加密技术，从而解锁新的使用案例

• 降低成本，同时通过快速直观的水平扩展增强可扩展性，进而实现高可用性

• 确保处理意外应用程序需求的韧性

在团队构建和管理应用程序的过程中，上述目标究竟会给他们带来哪些好处？首先，我们来探讨一下您为何应该使用 MongoDB 8.0。

无论您是 MongoDB 经验丰富的资深用户，还是新手用户，MongoDB 8.0 都可为您开发新应用程序和增强现有应用程序打下坚实基础。8.0 版不仅保留了最受开发者喜爱的一些 MongoDB 功能特性，比如提供直观统一的开发者体验，支持丰富的使用案例，确保操作的易用性，而且还大幅提升了性能表现。

改用 MongoDB 8.0 的主要理由

1. MongoDB 8.0 比前代产品速度快了 30% 以上

随着应用程序生成和使用的数据不断增加，即便效率小幅下滑也会导致基础设施的成本大幅攀升。由于不少客户主要通过应用程序与企业互动，如果应用程序性能不佳或有失稳定，可能会引起客户不满，导致错失机会、收入下降。因此，组织必须确保他们应用程序的表现始终如一。

MongoDB 8.0 可让应用程序快速高效地查询和转换数据，从而大大提升性能表现，将吞吐量最高增加 36%。MongoDB 8.0 进行了架构优化，降低了内存使用量，缩短了查询时间，通过更高效的批处理和优化，将数据复制期间的批量写入速度加快 56%，并发写入速度提高 20%。此外，MongoDB 8.0 中的优化意味着数据库可以处理更大量的时间序列数据，操作速度也能提高 200% 以上，而资源使用量和成本却不增反降。

2. MongoDB 8.0 比前代产品更加安全

数据保护和安全至关重要。随着跨环境传输、存储和处理的数据复杂性和数量不断增加，使用强大的加密技术保护敏感信息比以往任何时候都更加重要。组织必须确保在数据的整个生命周期中对其进行保护，包括确保数据在通过网络传输、静态存储以及查询和处理时的安全性。然而，在查询和处理数据时对其进行加密可能具有挑战性，数据容易被恶意行为者利用，造成暴露或外泄。

MongoDB Queryable Encryption 是 MongoDB 密码学研究小组开发的行业首创创新技术。该技术允许客户在客户端加密敏感数据，将其作为完全随机的加密数据安全地存储在MongoDB 数据库中，并对加密数据运行表达查询以进行处理。

MongoDB 8.0 除了支持等值查询之外，现在还支持范围查询，以扩展安全数据检索，为常用搜索提供更大的灵活性。借助 Queryable Encryption，所需数据将一直保持加密状态，直到到达使用客户控制的解密密钥的获授权最终用户处为止，而且无需用到任何加密专业知识。

3. MongoDB 8.0 让扩展变得更经济、更容易

随着组织不断发展壮大，其对应用程序的需求往往也会随之改变。例如，如果组织最初设计应用程序是为了供数千名用户使用，那么要想其扩展到支持数百万名用户可能就会比较困难。这是因为在生产应用程序中实施架构更改可能涉及大量的工作，而且成本高昂且耗费时间。

有了 MongoDB 8.0，实现水平扩展变得更快速、更简便，而且成本更低。通过水平扩展，应用程序可以突破传统数据库资源的限制，将数据拆分到多台服务器（称为分片）上，而无需为单台服务器预配越来越多的计算资源。MongoDB 8.0 中新增的分片功能可以将数据在分片中的分发速度提高最多 50 倍，并将初始成本降低 50%。

4. MongoDB 8.0 赋予您更多的控制权，助力您的应用程序顺利运行

应用程序的最终用户会希望即便在高需求期和使用高峰期，也能获得始终如一的使用体验。然而，如果没有高持久性的操作型数据库，应用程序在高需求时段会出现行为滞后（甚至停机）的情况，组织可能因此面临客户体验不佳的风险。

MongoDB 8.0 为优化数据库性能的团队赋予更大的控制权，以便应对难以预测的使用高峰期和持续高需求期。MongoDB 8.0 新增了各种功能，包括设置运行查询的默认最大时间限制，拒绝重复出现有问题的查询类型，以及设置查询设置以在数据库重启等事件中持久化。这些功能有助于确保应用程序始终保持一致的行为和较高的性能，不受需求激增或突发事件的影响。

准备好试用 MongoDB 8.0 了吗？

如果您要构建新的应用程序，MongoDB 8.0 最简单的上手方法就是访问 mongodb.com/try。您可以在此网站上免费注册 Atlas 帐户，下载 Community Edition，了解更多有关通过 Enterprise Advanced 订阅实现 MongoDB 自管理的信息。

如果您运行的是旧版 MongoDB，我们有关 MongoDB Atlas 和自管理部署的升级教程可以为您提供帮助。此外，MongoDB 的专业服务团队可随时提供文档和专家帮助。

如果您的现有应用程序暂未使用 MongoDB 数据库，请先了解 MongoDB Relational Migrator 工具。Relational Migrator 可以帮助您将现有关系模式映射到 MongoDB 模式，执行数据迁移，并将现有的关系查询、触发器和存储过程转换为适用于 MongoDB 的形式。

MongoDB 工程和产品团队认真听取了开发者的反馈，在构建 MongoDB 8.0 时将开发者可用性以及安全性、持久性、可用性和性能都放在了首位。我们期待您的试用，相信您将享受 MongoDB 8.0 带来的性能提升和其他各种好处！

전 세계적으로 수백만 명의 개발자와 50,000명 이상의 고객이 사용하는 세계에서 가장 인기 있는 문서 데이터베이스의 최신 버전인 MongoDB 8.0의 정식 출시 소식을 전해드리게 되어 기쁩니다. MongoDB 8.0은 업계를 선도하는 MongoDB의 기능을 기반으로 로컬 배포부터 엔터프라이즈 규모의 전 세계 분산 애플리케이션에 이르기까지 상당한 성능 향상, 비용 절감, 사용 편의성 향상을 제공합니다.

MongoDB가 오랫동안 개발자들의 사랑을 받아온만큼 8.0에서는 개발자 사용 편의성에 대한 기준을 매우 높게 유지했습니다. MongoDB 8.0은 고객의 가장 엄격한 보안, 복원력, 가용성 및 성능 요구 사항을 뛰어넘도록 만들어졌으며, 역대 가장 인상적인 MongoDB 버전입니다. MongoDB 8.0은 고객이 현재와 미래에 광범위한 애플리케이션을 구축할 수 있는 가장 강력한 기반을 제공합니다.

Jim Scharf, Chief Technology Officer, MongoDB

MongoDB 8.0에서는 다음과 같은 네 가지 핵심 목표를 중심으로 엔지니어링 노력을 집중했습니다.

• 다양한 애플리케이션에 맞게 성능 최적화

• 새로운 사용 사례 활용을 위한 혁신적인 암호화 제공

• 고가용성을 위해 빠르고 직관적인 수평 확장으로 비용 절감 및 규모 확대

• 예상치 못한 애플리케이션 수요에 대한 복원력 보장

그렇다면 이러한 목표는 팀이 애플리케이션을 구축하고 관리할 때 실제로 어떤 이점을 제공할까요? 먼저, MongoDB 8.0을 사용해야 하는 이유부터 살펴보겠습니다.

MongoDB의 숙련된 베테랑이든 데이터베이스를 처음 사용하는 초보자이든, MongoDB 8.0은 모두에게 새로운 애플리케이션을 구축하고 기존 애플리케이션을 강화하는 데 훌륭한 기반을 제공합니다. 버전 8.0은 직관적이고 일관된 개발자 환경, 광범위한 사용 사례 지원, 운영 편의성 등 개발자들이 가장 선호하는 MongoDB의 장점과 탁월한 성능 향상이 결합되었습니다.

MongoDB 8.0으로 전환해야 하는 주요 이유

1. MongoDB 8.0은 이전 버전보다 30% 이상 더 빠릅니다.

애플리케이션이 생성하고 사용하는 데이터가 증가함에 따라 사소한 비효율성이 인프라 비용의 불균형적인 증가로 이어질 수 있습니다. 많은 고객이 주로 애플리케이션을 통해 기업과 상호 작용하기 때문에 애플리케이션 성능이 좋지 않거나 일관되지 않으면 고객 불만, 기회 손실, 매출 감소로 이어질 수 있습니다. 따라서 조직은 애플리케이션이 일관되게 잘 작동하는지 확인하는 것이 중요합니다.

MongoDB 8.0은 애플리케이션이 데이터를 빠르고 효율적으로 쿼리하고 변환할 수 있도록 성능을 크게 개선하여 처리량을 최대 36%까지 향상시켰습니다. MongoDB 8.0의 아키텍처 최적화로 메모리 사용량과 쿼리 시간이 감소했으며, 보다 효율적인 일괄 처리와 최적화의 조합으로 데이터 복제 중 대량 쓰기 속도가 56% 빨라지고 동시 쓰기 속도가 20% 빨라졌습니다. 또한, MongoDB 8.0의 최적화를 통해 데이터베이스는 더 많은 양의 시계열 데이터를 처리하고 200% 이상 더 빠르게 작업을 수행할 수 있으며, 리소스 사용량과 비용은 더 낮출 수 있습니다.

2. MongoDB 8.0은 그 어느 때보다 더 안전합니다.

데이터 보호와 보안은 필수입니다. 다양한 환경에서 전송, 저장, 처리되는 데이터의 복잡성과 양이 증가함에 따라 강력한 암호화로 민감한 정보를 보호하는 것이 그 어느 때보다 중요해졌습니다. 조직에서는 네트워크에서 전송 중일 때, 저장된 위치에서 미사용 중일 때, 쿼리 및 처리에 사용되는 동안 등 데이터 수명 주기 전반에 걸쳐 데이터를 보호해야 합니다. 그러나 데이터를 쿼리하고 처리하는 동안 데이터를 암호화하는 것은 어려울 수 있으며, 이로 인해 악의적인 공격자가 데이터를 노출하거나 유출하는 데 취약할 수 있습니다.

MongoDB Queryable Encryption은 MongoDB 암호화 연구 그룹에서 개발한 업계 최초의 혁신입니다. 이 솔루션을 사용하면 고객은 민감한 클라이언트 사이드 데이터를 암호화하고, 이를 완전히 무작위로 암호화된 데이터로 MongoDB 데이터베이스에 안전하게 저장하고, 암호화된 데이터에 대해 처리를 위해 표현형 쿼리를 실행할 수 있습니다.

이제 MongoDB 8.0에는 동일성 쿼리 외에 범위 쿼리가 지원되어 일반적인 검색에서 더욱 유연하게 안전한 데이터 검색을 확장할 수 있습니다. Queryable Encryption을 사용하면 암호화에 대한 전문 지식 없이도 고객이 제어하는 암호 해독 키를 사용하여 인증된 최종 사용자에게 도달할 때까지 필수 데이터가 암호화된 상태로 유지됩니다.

3. MongoDB 8.0으로 더 저렴하고 쉽게 확장할 수 있습니다.

조직이 성장함에 따라 애플리케이션의 요구 사항도 진화하는 경향이 있습니다. 예를 들어, 원래 수천 명의 사용자를 위해 애플리케이션을 설계한 조직에서는 수백만 명의 사용자를 지원하도록 확장하는 것이 어려울 수 있습니다. 프로덕션 애플리케이션에서 아키텍처 변경을 구현하려면 상당한 노력이 필요하며 비용과 시간이 많이 소요될 수 있기 때문입니다.

MongoDB 8.0을 사용하면 수평적 확장이 더 빠르고 쉬워졌으며 비용도 더 저렴해졌습니다. 수평적 확장을 사용하면 애플리케이션은 단일 서버에 점점 더 많은 양의 컴퓨팅 리소스를 미리 프로비저닝하지 않고도 데이터를 샤드라고 알려진 여러 서버에 분할하여 기존 데이터베이스 리소스의 한계를 넘어 확장할 수 있습니다. MongoDB 8.0의 새로운 샤딩 기능은 샤드 간에 데이터를 최대 50배 더 빠르게 분산하고 시작 비용을 최대 50% 낮춰줍니다.

4. MongoDB 8.0은 애플리케이션이 원활하게 실행되도록 더 많은 제어 기능을 제공합니다.

최종 사용자는 수요가 많고 사용량이 급증하는 시기에도 일관된 애플리케이션 경험을 기대합니다. 운영 데이터베이스의 내구성이 뛰어나지 않은 조직은 수요가 많은 시간대에 애플리케이션 동작이 지연되거나 다운타임이 발생하는 등 고객 경험이 저하될 위험이 있습니다.

MongoDB 8.0은 예측할 수 없는 사용량 급증과 지속적인 수요 증가에 대비해 데이터베이스 성능을 최적화하는 팀에게 더 강력한 제어 기능을 제공합니다. MongoDB 8.0에는 쿼리 실행에 대한 기본 최대 시간 제한을 설정하고, 문제가 있는 쿼리 유형을 반복적으로 거부하고, 데이터베이스 재시작과 같은 이벤트에도 쿼리 설정이 지속되도록 설정하는 새로운 기능이 포함되어 있습니다. 이러한 기능은 수요 급증이나 예상치 못한 이벤트에 관계없이 일관된 애플리케이션 동작과 높은 성능을 제공하는 데 도움이 됩니다.

MongoDB 8.0을 사용해 볼 준비가 되셨나요?

새 애플리케이션을 구축하는 경우, MongoDB 8.0을 시작하는 가장 쉬운 방법은 mongodb.com/try로 이동하는 것입니다. 여기에서 무료 Atlas 계정에 가입하고, Community edition을 다운로드하고, Enterprise Advanced 구독을 통해 MongoDB를 직접 관리하는 방법에 대해 자세히 알아볼 수 있습니다.

이전 버전의 MongoDB를 실행 중인 경우, MongoDB Atlas 및 자체 관리형 배포를 위한 유용한 업그레이드 튜토리얼이 마련되어 있습니다. 또한, MongoDB 전문 서비스 팀에서 제공하는 문서와 전문가 지원도 받으실 수 있습니다.

현재 MongoDB를 데이터베이스로 사용하지 않는 기존 애플리케이션이 있는 경우, MongoDB Relational Migrator 도구를 확인해 보세요. Relational Migrator를 사용하면 기존 관계형 스키마를 MongoDB 스키마에 매핑하고, 데이터 마이그레이션을 수행하고, 기존 관계형 쿼리, 트리거 및 저장 프로시저를 MongoDB에서 작동하도록 변환할 수 있습니다.

MongoDB 엔지니어링 및 제품 팀은 개발자의 피드백에 귀를 기울여 보안, 내구성, 가용성, 성능은 물론 개발자의 사용 편의성을 최우선으로 고려하여 MongoDB 8.0을 구축했습니다. 한 번 사용해 보시고, 성능 향상 및 다양한 MongoDB 8.0의 이점을 누리실 수 있기를 기대합니다!

Siamo lieti di annunciare che MongoDB 8.0, la versione più recente del document database più diffuso al mondo, utilizzata da milioni di sviluppatori e da più di 50.000 clienti in tutto il mondo, è ora in general availability. MongoDB 8.0 si basa sulle funzionalità leader del settore di MongoDB, rendendo possibili notevoli miglioramenti delle prestazioni, una riduzione dei costi e una maggiore facilità d'uso, dalle implementazioni locali alle applicazioni distribuite a livello globale su scala aziendale.

Lo sviluppo con MongoDB è da tempo apprezzato dagli sviluppatori, quindi ci siamo assicurati che la versione 8.0 mantenesse un livello estremamente elevato per la fruibilità degli sviluppatori. MongoDB 8.0 è stato creato anche per superare i requisiti di sicurezza, resilienza, disponibilità e prestazioni più rigorosi dei nostri clienti ed è la versione di MongoDB più sorprendente di sempre. MongoDB 8.0 offre ai clienti la base più solida possibile per creare un'ampia gamma di applicazioni, ora e in futuro.

Jim Scharf, Chief Technology Officer, MongoDB

Per MongoDB 8.0, abbiamo concentrato il nostro impegno di progettazione su quattro obiettivi principali:

• Ottimizzare le prestazioni per la più ampia varietà di applicazioni

• Fornire una crittografia innovativa per consentire nuovi casi d'uso

• Ridurre i costi e aumentare la scala, con scalabilità orizzontale rapida e intuitiva per l'elevata disponibilità

• Garantire resilienza in caso di domanda imprevista delle applicazioni

In che modo questi obiettivi avvantaggiano effettivamente i team che creano e gestiscono le applicazioni? Inizieremo esaminando il motivo per cui consigliamo di usare MongoDB 8.0.

Sia per gli esperti di MongoDB che per i neofiti del database, MongoDB 8.0 costituisce una base eccellente per nuove applicazioni e per potenziare quelle esistenti. La versione 8.0 combina ciò che gli sviluppatori amano di più di MongoDB, come un'esperienza di sviluppo intuitiva e coesa, l'assistenza per un'ampia gamma di casi d'uso e la facilità d'uso operativa, con miglioramenti delle prestazioni senza precedenti.

Principali motivi per passare a MongoDB 8.0

1. MongoDB 8.0 è più veloce del 30% rispetto al passato

Con l'incremento dei dati generati e utilizzati dalle applicazioni, piccole inefficienze possono portare a un aumento sproporzionato dei costi dell'infrastruttura. Poiché molti clienti interagiscono principalmente con le aziende tramite le loro applicazioni, prestazioni scarse o incoerenti delle applicazioni possono portare a insoddisfazione dei clienti, perdita di opportunità e diminuzione dei ricavi. Pertanto, è fondamentale che le organizzazioni garantiscano che le loro applicazioni funzionino bene in modo continuativo.

MongoDB 8.0 migliora notevolmente le prestazioni consentendo alle applicazioni di interrogare e trasformare i dati in modo rapido ed efficiente, con un throughput fino al 36% migliore. Le ottimizzazioni dell'architettura in MongoDB 8.0 hanno ridotto l'utilizzo della memoria e i tempi delle query e una combinazione di elaborazione e ottimizzazioni batch più efficienti consente scritture in blocco più veloci del 56% e scritture simultanee più veloci del 20% durante la replica dei dati. Inoltre, grazie alle ottimizzazioni, il database MongoDB 8.0 è in grado di gestire volumi più elevati di dati di time-series ed eseguire operazioni oltre il 200% più velocemente, riducendo l'utilizzo e il costo delle risorse.

2. MongoDB 8.0 è più sicuro che mai

La protezione e la sicurezza dei dati sono essenziali. Con l'aumento della complessità e del volume dei dati trasmessi, archiviati ed elaborati negli ambienti, la salvaguardia delle informazioni sensibili con una crittografia solida è più che mai fondamentale. Le organizzazioni devono proteggere i dati durante l'intero ciclo di vita, in transito sulle reti, nei luoghi inattivi in cui sono archiviati e mentre sono in uso per l'esecuzione e l'elaborazionedi query. Tuttavia, può essere difficile crittografare i dati mentre vengono interrogati ed elaborati e questo li rende vulnerabili all'esposizione o all'esfiltrazione da parte di malintenzionati.

MongoDB Queryable Encryption è una novità assoluta nel settore, sviluppata dal MongoDB Cryptography Research Group. Consente ai clienti di crittografare i dati sensibili sul lato client, archiviarli in modo sicuro come dati crittografati completamente randomizzati nel database MongoDB ed eseguire query espressive sui dati crittografati per l'elaborazione.

MongoDB 8.0 ora include il supporto per le query sugli intervalli, oltre alle query di uguaglianza, per espandere il recupero sicuro dei dati con una maggiore flessibilità per le ricerche comuni. Con Queryable Encryption, i dati richiesti rimangono crittografati fino a quando non raggiungono un utente finale autorizzato, utilizzando una chiave di decrittografia controllata dal cliente, senza la necessità di competenze crittografiche.

3. MongoDB 8.0 rende più economico e facile scalare

Man mano che le organizzazioni crescono, i requisiti delle loro applicazioni tendono a evolversi. Ad esempio, la scalabilità per supportare milioni di utenti può essere impegnativa per le organizzazioni che originariamente avevano progettato le loro applicazioni per migliaia di utenti. Questo perché l'implementazione di modifiche architettoniche nelle applicazioni di produzione può comportare impegno notevole e dispendioso in termini di tempo e risorse.

Con MongoDB 8.0, la scalabilità orizzontale è ora più rapida e semplice e ha un costo inferiore. Grazie alla scalabilità orizzontale, le applicazioni possono espandersi oltre i limiti delle risorse di database tradizionali, suddividendo i dati su più server, noti come shard, senza dover effettuare il provisioning preliminare di una quantità di risorse di elaborazione sempre maggiore per un solo server. Le nuove funzionalità di partizionamento orizzontale di MongoDB 8.0 distribuiscono i dati tra gli shard fino a 50 volte più velocemente e con costi iniziali inferiori fino al 50%.

4. MongoDB 8.0 offre un maggiore controllo, per favorire il funzionamento fluido delle applicazioni

Gli utenti finali si attendono esperienze delle applicazioni coerenti, anche nei periodi di elevata domanda e di picchi di utilizzo. Le organizzazioni senza un database operativo altamente durevole rischiano di produrre esperienze del cliente scadenti, determinando ritardi delle applicazioni (o addirittura tempi di inattività) nei periodi di incremento della domanda.

MongoDB 8.0 offre maggiore controllo per i team, ottimizzando le prestazioni del database, per picchi di utilizzo imprevedibili e periodi prolungati di forte domanda. MongoDB 8.0 include nuove funzionalità per impostare un limite di tempo massimo predefinito per l'esecuzione delle query, rifiutare tipi ricorrenti di query problematiche e configurare le impostazioni delle query in modo che rimangano definite durante eventi come il riavvio del database. Queste funzionalità consentono di garantire un comportamento coerente delle applicazioni e prestazioni elevate, indipendentemente dai picchi di domanda o dagli eventi imprevisti.

Vuoi provare MongoDB 8.0?

Se stai creando una nuova applicazione, il modo più semplice per iniziare con MongoDB 8.0 è visitare mongodb.com/try, dove è possibile creare un account Atlas gratuito, scaricare l'edizione Community e scoprire di più sulla gestione automatica di MongoDB con un abbonamento Enterprise Advanced.

Se utilizzi una versione precedente di MongoDB, sono disponibili utili tutorial di aggiornamento per MongoDB Atlas e distribuzioni autogestite. Inoltre, sono disponibili la documentazione e l'assistenza di esperti del team di servizi professionali di MongoDB.

Se hai un'applicazione già attiva che attualmente non utilizza MongoDB come database, valuta lo strumento MongoDB Relational Migrator. Relational Migrator può aiutare a mappare gli schemi relazionali già presenti su uno schema MongoDB, eseguire migrazioni di dati e convertire le query relazionali, i trigger e le procedure memorizzate già presenti per funzionare con MongoDB.

I team di progettazione e prodotto di MongoDB hanno ascoltato attentamente il feedback degli sviluppatori e MongoDB 8.0 è stato creato pensando alla fruibilità degli sviluppatori, oltre che alla sicurezza, alla durata, alla disponibilità e alle prestazioni. Invitiamo a provarlo e siamo sicuri che apprezzerai i miglioramenti delle prestazioni e gli altri vantaggi di MongoDB 8.0.

Nous sommes ravis d’annoncer que MongoDB 8.0, la nouvelle version de la base de données documentaire la plus populaire au monde, utilisée par des millions de développeurs et plus de 50 000 clients dans le monde entier, est désormais disponible. Des déploiements locaux aux applications d’entreprise déployées dans le monde entier, elle s’appuie sur les capacités de pointe de MongoDB pour optimiser les performances, réduire les coûts et simplifier l’utilisation.

Les développeurs utilisent MongoDB depuis longtemps, nous avons donc veillé à ce que la convivialité reste optimale. MongoDB 8.0 a également été conçue pour répondre aux exigences les plus strictes en matière de sécurité, de résilience, de disponibilité et de performance. C’est la version la plus aboutie de MongoDB à ce jour. Elle offre à nos clients la base la plus solide pour construire une large gamme d’applications, aujourd’hui et à l’avenir.

Jim Scharf, Chief Technology Officer, MongoDB

Pour cette nouvelle version, nous nous sommes fixé quatre objectifs principaux :

• optimiser les performances pour un maximum d’applications ;

• proposer une solution de chiffrement innovante pour créer de nouveaux cas d’utilisation ;

• réduire les coûts et accroître la scalabilité grâce à une mise à l’échelle horizontale rapide et intuitive pour une haute disponibilité ;

• garantir la résilience face à une demande d’application inattendue.

En quoi ces objectifs profitent-ils réellement aux équipes lorsqu’elles créent et gèrent des applications ? Commençons par voir pourquoi vous devriez utiliser MongoDB 8.0.

Que vous soyez un néophyte ou expert en la matière, MongoDB 8.0 est une excellente base pour les nouvelles applications et pour améliorer les applications existantes. Cette version combine les fonctionnalités les plus appréciées des développeurs, à savoir une expérience intuitive et cohérente, la prise en charge de nombreux cas d’utilisation et une facilité d’utilisation opérationnelle, avec des performances inégalées.

Principales raisons de passer à MongoDB 8.0

1. Cette version est plus de 30 % plus rapide qu’auparavant

À mesure que les données générées et utilisées par les applications augmentent, des inefficacités mineures peuvent entraîner des hausses disproportionnées des coûts d’infrastructure. Étant donné que de nombreux clients interagissent avec les entreprises via leurs applications, des performances médiocres ou incohérentes peuvent être source d’insatisfaction, faire perdre des opportunités et entraîner une baisse du chiffre d’affaires. Les entreprises doivent donc s’assurer que leurs applications fonctionnent toujours correctement.

MongoDB 8.0 améliore considérablement les performances des applications. En effet, elle optimise l’interrogation et la transformation des données, avec un débit jusqu’à 36 % supérieur. Les optimisations architecturales ont réduit l’utilisation de la mémoire et les temps d’interrogation. De plus, le traitement par lots plus avancé et les optimisations a permis d’accélérer de 56 % les opérations d’écriture en masse et de 20 % les opérations d’écriture simultanées lors de la réplication des données. En outre, la base de données peut traiter des volumes plus importants de données de time series et effectuer des opérations plus de 200 % plus rapidement, tout en utilisant moins de ressources et en réduisant les coûts.

2. MongoDB 8.0 n’a jamais été aussi sécurisée

La protection et la sécurité des données sont des enjeux clés. Face à la complexité et au volume croissants des données transmises, stockées et traitées dans différents environnements, il est plus que jamais essentiel de protéger les informations sensibles avec un chiffrement renforcé. Les entreprises doivent protéger leurs données tout au long de leur cycle de vie : pendant leur transit sur les réseaux, au repos lorsqu’elles sont stockées, et pendant leur utilisation pour les requêtes et le traitement. Cependant, il peut être difficile de chiffrer les données lorsqu’elles sont interrogées et traitées. Elles sont donc susceptibles d’être exposées ou exfiltrées par des personnes mal intentionnées.

MongoDB Queryable Encryption est une solution innovante développée par le MongoDB Cryptography Research Group. Elle permet de chiffrer les données sensibles côté client, de les stocker en toute sécurité sous forme de données chiffrées entièrement randomisées dans la base de données MongoDB, et d’exécuter des requêtes expressives sur les données chiffrées à des fins de traitement.

Afin d’étendre la récupération sécurisée des données avec une plus grande flexibilité pour les recherches courantes, cette nouvelle version prend désormais en charge les requêtes d’égalité et les requêtes de plages. Grâce à Queryable Encryption, les données requises restent chiffrées jusqu’à ce qu’elles parviennent à un utilisateur final autorisé à l’aide d’une clé de déchiffrement contrôlée par le client. Aucune expertise en cryptographie n’est nécessaire.

3. MongoDB 8.0 facilite la scalabilité à moindre coût

À mesure que les entreprises se développent, les besoins de leurs applications ont tendance à évoluer. Par exemple, la prise en charge de millions d’utilisateurs peut représenter un défi pour les entreprises qui ont initialement conçu leurs applications pour des milliers d’utilisateurs. En effet, la mise en œuvre de changements architecturaux dans les applications de production peut impliquer des efforts considérables qui peuvent s’avérer coûteux et chronophages.

Avec MongoDB 8.0, la mise à l’échelle horizontale est désormais plus rapide, plus facile et moins onéreuse. Les applications peuvent dépasser les limites des ressources de base de données traditionnelles en répartissant les données sur plusieurs serveurs appelés shards, sans avoir à préprovisionner des quantités croissantes de ressources de calcul pour un seul serveur. Les nouvelles fonctionnalités de sharding distribuent les données sur les shards jusqu’à 50 fois plus vite, à un coût de démarrage jusqu’à 50 % inférieur.

4. MongoDB 8.0 vous permet de mieux contrôler le fonctionnement de vos applications

Les utilisateurs finaux s’attendent à des expériences d’application cohérentes, même pendant les périodes de forte demande et les pics d’utilisation. Les entreprises qui ne disposent pas d’une base de données opérationnelle ultra-performante risquent de nuire à l’expérience client, car leurs applications pourront subir des retards voire ne plus fonctionner en période de forte demande.

Cette version offre un meilleur contrôle aux équipes qui optimisent les performances des bases de données en cas de pics d’utilisation imprévus et pendant les périodes de forte demande prolongées. Elle comprend de nouvelles fonctionnalités permettant de définir une limite de temps maximale par défaut pour l’exécution des requêtes, de rejeter les types récurrents de requêtes problématiques et de définir des paramètres de requêtes persistants en cas d’événements tels que le redémarrage de la base de données. Elles permettent de garantir la cohérence des applications et des performances élevées, même en cas de forte demande ou d’événements inattendus.

Prêt à vous lancer ?

Si vous créez une nouvelle application, le plus simple est de vous rendre sur le site mongodb.com/try. Vous pourrez créer un compte Atlas gratuit, télécharger Community Edition et obtenir de plus amples informations sur la gestion autonome de MongoDB avec un abonnement MongoDB Enterprise Advanced.

Si vous utilisez une version antérieure, vous trouverez des tutoriels de mise à niveau utiles pour MongoDB Atlas et les déploiements autogérés. En outre, vous pourrez également consulter la documentation et demande l’aide de l’équipe de services professionnels MongoDB.

Si votre application existante n’utilise pas la base de données MongoDB, consultez l’outil MongoDB Relational Migrator. Il peut vous aider à mapper des schémas relationnels existants à un schéma MongoDB, à effectuer des migrations de données et à convertir des requêtes relationnelles, des triggers et des procédures pour garantir leur compatibilité avec MongoDB.

Les ingénieurs et l’équipe produits de MongoDB ont écouté attentivement les commentaires des développeurs. MongoDB 8.0 a été conçue dans un objectif de convivialité, sécurité, durabilité, disponibilité et performance. Nous avons hâte que vous l’essayiez et nous sommes sûrs que vous apprécierez les gains de productivité et les autres avantages de MongoDB 8.0 !

Nos complace anunciar que MongoDB 8.0, la versión más reciente de la base de datos de documentos más popular del mundo, utilizada por millones de desarrolladores y más de 50.000 clientes en todo el mundo, ya está disponible en general. MongoDB 8.0 se basa en las capacidades líderes de la industria de MongoDB para proporcionar mejoras significativas en el rendimiento, costos reducidos y mayor facilidad de uso, desde implementaciones locales hasta aplicaciones distribuidas globalmente a escala empresarial.

A los desarrolladores les encanta construir con MongoDB, así que nos hemos asegurado de que 8.0 mantenga el listón extremadamente alto para la usabilidad del desarrollador. MongoDB 8.0 también se creó para superar los requisitos de seguridad, resistencia, disponibilidad y rendimiento más estrictos de nuestros clientes, y es la versión más impresionante de MongoDB hasta el momento. MongoDB 8.0 ofrece a los clientes la base más estable posible para crear una amplia gama de aplicaciones, ahora y en el futuro.

Jim Scharf, Chief Technology Officer, MongoDB

Para MongoDB 8.0, centramos nuestros esfuerzos de ingeniería en torno a cuatro objetivos principales:

• Optimice el rendimiento para la más amplia variedad de aplicaciones

• Ofrezca un cifrado innovador para desbloquear nuevos casos de uso

• Reduzca los costos y aumente la escala con un escalado horizontal rápido e intuitivo para una alta disponibilidad

• Garantice la resiliencia ante la demanda inesperada de aplicaciones

Entonces, ¿cómo benefician realmente estos objetivos a los equipos a medida que crean y gestionan aplicaciones? Comenzaremos por ver por qué debería usar MongoDB 8.0.

Tanto si es un veterano experimentado de MongoDB como si es nuevo en la base de datos, MongoDB 8.0 es una gran base para nuevas aplicaciones y para la sobrecarga de las existentes por igual. La versión 8.0 combina lo que más les gusta a los desarrolladores de MongoDB, como una experiencia de desarrollador intuitiva y cohesiva, soporte para un amplio conjunto de casos de uso y facilidad de uso operacional, con mejoras de rendimiento sin precedentes.

Principales razones para cambiar a MongoDB 8.0

1. MongoDB 8.0 es más de un 30% más rápido que antes

A medida que crecen los datos que generan y emplean, las ineficiencias menores pueden provocar aumentos desproporcionados en los costos de infraestructura. Debido a que muchos clientes interactúan principalmente con las empresas a través de sus aplicaciones, el rendimiento deficiente o inconsistente de las aplicaciones puede llevar a la insatisfacción del cliente, la pérdida de oportunidades y la disminución de los ingresos. Por lo tanto, es imperativo que las organizaciones se cercioren de que sus aplicaciones funcionen bien de manera constante.

MongoDB 8.0 mejora significativamente el rendimiento al permitir que las aplicaciones consulten y transformen datos de manera rápida y eficiente, con hasta un 36% mejor de rendimiento. Las optimizaciones arquitectónicas en MongoDB 8.0 han reducido el uso de memoria y los tiempos de consulta, y una combinación de procesamiento por lotes y optimizaciones más eficientes ha permitido escrituras masivas 56% más rápidas y escrituras simultáneas un 20% más rápidas durante la replicación de datos. Además, las optimizaciones de MongoDB 8.0 significan que la base de datos puede manejar mayores volúmenes de datos de series temporales y realizar operaciones más de un 200% más rápido, con un menor uso de recursos y costos.

2. MongoDB 8.0 es más seguro que nunca

La protección de datos y la seguridad son esenciales. Con la creciente complejidad y el volumen de datos que se transmiten, almacenan y procesan en todos los entornos, proteger la información confidencial con un cifrado sólido es más crítico que nunca. Las organizaciones deben proteger sus datos a lo largo de su ciclo de vida: en tránsito a través de las redes, en reposo donde se almacenan y mientras se emplean para consultas y procesamiento. Sin embargo, puede ser difícil cifrar los datos mientras se consultan y procesan, dejando los datos vulnerables a la exposición o exfiltración por parte de actores malintencionados.

MongoDB Queryable Encryption es una innovación líder en la industria desarrollada por el Grupo de Investigación de Criptografía MongoDB. Permite a los clientes cifrar datos confidenciales del lado del cliente, almacenarlos de forma segura como datos cifrados completamente aleatorios en la base de datos MongoDB y ejecutar consultas expresivas sobre los datos cifrados para su procesamiento.

MongoDB 8.0 ahora incluye soporte para consultas de rango, además de consultas de igualdad, para ampliar la recuperación segura de datos con mayor flexibilidad para búsquedas comunes. Con Queryable Encryption, los datos requeridos permanecen cifrados hasta que llegan a un usuario final autorizado utilizando una clave de descifrado controlada por el cliente, sin necesidad de experiencia en criptografía.

3. MongoDB 8.0 hace que sea más barato y fácil de escalar

A medida que las organizaciones crecen, los requisitos de sus aplicaciones tienden a evolucionar. Por ejemplo, escalar para dar soporte a millones de usuarios puede ser un desafío para las organizaciones que originalmente diseñaron sus aplicaciones para miles de usuarios. Esto se debe a que la implementación de cambios arquitectónicos en las aplicaciones de producción puede implicar un esfuerzo significativo que puede ser costoso y llevar mucho tiempo.

Con MongoDB 8.0, el escalado horizontal ahora es más rápido y más fácil, y a un costo menor. Con el escalado horizontal, las aplicaciones pueden escalar más allá de los límites de los recursos de bases de datos tradicionales mediante la división de datos en varios servidores conocidos como fragmentos, sin tener que aprovisionar previamente cantidades crecientes de recursos informáticos para un solo servidor. Las nuevas capacidades de sharding de MongoDB 8.0 distribuyen los datos entre shards hasta 50 veces más rápido y con un costo de puesta en marcha hasta un 50% menor.

4. MongoDB 8.0 le brinda más control para ayudar a que sus aplicaciones funcionen sin problemas

Los usuarios finales esperan experiencias de aplicación consistentes, incluso durante periodos de alta demanda y picos de uso. Las organizaciones sin una base de datos operativa altamente duradera corren el riesgo de experiencias deficientes del cliente, con un comportamiento de aplicación retrasado (o incluso tiempo de inactividad) durante tiempos de alta demanda.

MongoDB 8.0 proporciona un mayor control para los equipos, optimizando el rendimiento de la base de datos para picos impredecibles en el uso y periodos sostenidos de alta demanda. MongoDB 8.0 incluye nuevas capacidades para establecer un límite de tiempo máximo predeterminado para ejecutar consultas, rechazar tipos recurrentes de consultas problemáticas y establecer la configuración de consulta para que persista a través de eventos como reinicios de la base de datos. Estas capacidades ayudan a ofrecer un comportamiento coherente de las aplicaciones y un alto rendimiento, independientemente de los picos de demanda o los eventos inesperados.

¿Listo para probar MongoDB 8.0?

Si está creando una nueva aplicación, la manera más fácil de comenzar con MongoDB 8.0 es yendo a mongodb.com/try, donde puede inscribirse para obtener una cuenta Atlas gratuita, descargar la edición Community y obtener más información sobre la autogestión de MongoDB con una suscripción Enterprise Advanced.

Si está ejecutando una versión anterior de MongoDB, hay útiles tutoriales de actualización para MongoDB Atlas e implementaciones autoadministradas. Además, la documentación y la ayuda experta del equipo de servicios profesionales de MongoDB están a su disposición.

Si tiene una aplicación existente que actualmente no utiliza MongoDB como base de datos, consulte la herramienta MongoDB Relational Migrator. Relational Migrator puede ayudarle a mapear esquemas relacionales existentes a un esquema MongoDB, realizar migraciones de datos y convertir consultas relacionales existentes, activadores y procedimientos almacenados para trabajar con MongoDB.

Los equipos de ingeniería y productos de MongoDB escucharon atentamente los comentarios de los desarrolladores, y MongoDB 8.0 se creó teniendo en cuenta la usabilidad de los desarrolladores, así como la seguridad, la durabilidad, la disponibilidad y el rendimiento. Estamos entusiasmados de que lo pruebe y estamos seguros de que disfrutará de las ganancias de rendimiento y otros beneficios de MongoDB 8.0.

Wir freuen uns, Ihnen mitteilen zu können, dass MongoDB 8.0 – die neueste Version der weltweit beliebtesten Dokumentdatenbank, die von Millionen Entwicklern und mehr als 50.000 Kunden auf der ganzen Welt verwendet wird – jetzt allgemein verfügbar ist. MongoDB 8.0 baut auf den branchenführenden Funktionen von MongoDB auf und bietet erhebliche Leistungsverbesserungen, geringere Kosten und eine höhere Benutzerfreundlichkeit, von der lokalen Bereitstellung bis hin zu global verteilten Anwendungen auf Unternehmensebene.

Entwickler lieben es schon lange, mit MongoDB zu arbeiten, daher haben wir sichergestellt, dass 8.0 den hohen Standard der Benutzerfreundlichkeit für Entwickler beibehält. MongoDB 8.0 wurde außerdem entwickelt, um die strengsten Sicherheits-, Ausfallsicherheits-, Verfügbarkeits- und Leistungsanforderungen unserer Kunden zu übertreffen und ist die bislang beeindruckendste Version von MongoDB. MongoDB 8.0 bietet Kunden die bestmögliche Grundlage für die Erstellung einer breiten Palette von Anwendungen – jetzt und in der Zukunft.

Jim Scharf, Chief Technology Officer, MongoDB

Für MongoDB 8.0 haben wir unsere Entwicklungsbemühungen auf vier Kernziele konzentriert:

• Optimierung der Leistung für die unterschiedlichsten Anwendungen

• Innovative Verschlüsselung, um neue Anwendungsfälle zu erschließen

• Reduzierung der Kosten und Steigerung der Skalierbarkeit durch schnelle und intuitive horizontale Skalierung für hohe Verfügbarkeit

• Sicherstellen der Ausfallsicherheit bei unerwarteter Anwendungsnachfrage

Wie profitieren Teams also tatsächlich von diesen Zielen beim Erstellen und Verwalten von Anwendungen? Sehen wir uns zunächst an, warum Sie MongoDB 8.0 verwenden sollten.

Unabhängig davon, ob Sie ein erfahrener MongoDB-Nutzer sind oder sich zum ersten Mal mit der Datenbank beschäftigen: MongoDB 8.0 ist eine großartige Grundlage für neue Anwendungen und zur Optimierung bestehender Anwendungen. Version 8.0 kombiniert die Dinge, die Entwickler an MongoDB am meisten lieben – wie ein intuitives und einheitliches Entwicklererlebnis, Unterstützung für eine Vielzahl von Anwendungsfällen und einfache Bedienung – mit beispiellosen Leistungsverbesserungen.

Die wichtigsten Gründe für den Wechsel zu MongoDB 8.0

1. MongoDB 8.0 ist über 30 % schneller als zuvor

Da die Datenanwendungen immer mehr Daten generieren und nutzen, können geringfügige Ineffizienzen zu einem überproportionalen Anstieg der Infrastrukturkosten führen. Da viele Kunden in erster Linie über ihre Anwendungen mit Unternehmen interagieren, kann eine schlechte oder inkonsistente Anwendungsleistung zu Kundenunzufriedenheit, verpassten Geschäftschancen und Umsatzrückgängen führen. Daher ist es für Unternehmen unerlässlich, sicherzustellen, dass ihre Anwendungen konstant gut funktionieren.

MongoDB 8.0 verbessert die Leistung erheblich, indem es Anwendungen ermöglicht, Daten schnell und effizient abzufragen und zu transformieren, mit bis zu 36 % besserem Durchsatz. Durch Architekturoptimierungen in MongoDB 8.0 wurden Speichernutzung und Abfragezeiten reduziert, und eine Kombination aus effizienterer Stapelverarbeitung und Optimierungen ermöglichte 56 % schnellere Massenschreibvorgänge und 20 % schnellere gleichzeitige Schreibvorgänge bei der Datenreplikation. Darüber hinaus bedeuten Optimierungen in MongoDB 8.0, dass die Datenbank größere Mengen an Zeitreihendaten verarbeiten und Vorgänge über 200 % schneller ausführen kann – bei geringerem Ressourcenverbrauch und geringeren Kosten.

2. MongoDB 8.0 ist sicherer als je zuvor

Datenschutz und -sicherheit sind unerlässlich. Angesichts der zunehmenden Komplexität und des Volumens der Daten, die zwischen verschiedenen Umgebungen übertragen, gespeichert und verarbeitet werden, ist der Schutz vertraulicher Informationen durch eine robuste Verschlüsselung wichtiger denn je. Organisationen müssen ihre Daten während des gesamten Lebenszyklus schützen – während der Übertragung über Netzwerke, im Ruhezustand am Speicherort und während der Verwendung für Abfragen und Verarbeitung. Es kann jedoch schwierig sein, Daten während der Abfrage und Verarbeitung zu verschlüsseln, so dass die Daten durch böswillige Akteure aufgedeckt oder exfiltriert werden können.

MongoDB Queryable Encryption ist eine branchenweit einzigartige Innovation, die von der MongoDB Cryptography Research Group entwickelt wurde. Sie ermöglicht Kunden, vertrauliche Daten clientseitig zu verschlüsseln, sie sicher als vollständig randomisierte verschlüsselte Daten in der MongoDB-Datenbank zu speichern und aussagekräftige Abfragen auf den verschlüsselten Daten zur Verarbeitung auszuführen.

MongoDB 8.0 unterstützt jetzt – zusätzlich zu Gleichheitsabfragen – auch Bereichsabfragen, um den sicheren Datenabruf mit größerer Flexibilität für allgemeine Suchvorgänge zu erweitern. Mit Queryable Encryption bleiben die erforderlichen Daten verschlüsselt, bis sie mithilfe eines vom Kunden kontrollierten Entschlüsselungsschlüssels einen autorisierten Endbenutzer erreichen – ohne dass hierfür Kenntnisse in der Kryptografie erforderlich sind.

3. MongoDB 8.0 macht Skalierung günstiger und einfacher

Wenn Unternehmen wachsen, entwickeln sich auch die Anforderungen an ihre Anwendungen weiter. Beispielsweise kann die Skalierung auf Millionen von Benutzern eine Herausforderung für Unternehmen darstellen, die ihre Anwendungen ursprünglich für Tausende von Benutzern konzipiert haben. Der Grund dafür ist, dass die Implementierung von Architekturänderungen in Produktionsanwendungen mit erheblichem, mitunter kostspieligem und zeitraubendem Aufwand verbunden sein kann.

Mit MongoDB 8.0 ist die horizontale Skalierung jetzt schneller, einfacher und günstiger. Mit horizontaler Skalierung können Anwendungen über die Grenzen herkömmlicher Datenbankressourcen hinaus skaliert werden, indem die Daten auf mehrere Server (sogenannte Shards) aufgeteilt werden – ohne dass für einen einzelnen Server vorab immer mehr Rechenressourcen bereitgestellt werden müssen. Neue Sharding-Funktionen in MongoDB 8.0 verteilen Daten bis zu 50-mal schneller auf Shards und bieten beim Einstieg bis zu 50 % geringere Kosten.

4. MongoDB 8.0 gibt Ihnen mehr Kontrolle, damit Ihre Anwendungen reibungslos laufen

Endbenutzer erwarten konsistente Anwendungserlebnisse, selbst in Zeiten hoher Nachfrage und während Nutzungsspitzen. Unternehmen, die nicht über eine langlebige operative Datenbank verfügen, riskieren schlechte Kundenerfahrungen und ein verzögertes Anwendungsverhalten (oder sogar Ausfallzeiten) in Zeiten hoher Nachfrage.

MongoDB 8.0 bietet Teams, die die Datenbankleistung für unvorhersehbare Nutzungsspitzen und anhaltend hohe Nachfrage optimieren, mehr Kontrolle. MongoDB 8.0 enthält neue Funktionen zum Festlegen eines standardmäßigen maximalen Zeitlimits für die Ausführung von Abfragen, zum Ablehnen wiederkehrender Typen problematischer Abfragen und zum Festlegen von Abfrageeinstellungen, die auch bei Ereignissen wie Datenbankneustarts bestehen bleiben. Diese Funktionen tragen dazu bei, ein konsistentes Anwendungsverhalten und eine hohe Leistung sicherzustellen, unabhängig von Nachfragespitzen oder unerwarteten Ereignissen.

Sind Sie bereit, MongoDB 8.0 auszuprobieren?

Wenn Sie eine neue Anwendung erstellen, können Sie am einfachsten mit MongoDB 8.0 beginnen, indem Sie mongodb.com/try aufrufen. Hier können Sie sich für ein kostenloses Atlas-Konto anmelden, die Community Edition herunterladen und mit einem Enterprise-Advanced-Abonnement mehr über die selbstverwaltete MongoDB erfahren.

Wenn Sie eine frühere Version von MongoDB verwenden, finden Sie hilfreiche Upgrade-Tutorials für MongoDB Atlas und selbstverwaltete Implementierungen. Darüber hinaus stehen die Dokumentation und die fachkundige Hilfe des MongoDB Professional Services Teams zur Verfügung.

Wenn Sie über eine vorhandene Anwendung verfügen, die derzeit nicht MongoDB als Datenbank verwendet, sehen Sie sich das Tool MongoDB Relational Migrator an. Relational Migrator kann Ihnen dabei helfen, vorhandene relationale Schemata einem MongoDB-Schema zuzuordnen, Datenmigrationen durchzuführen und vorhandene relationale Abfragen, Trigger und gespeicherte Prozeduren für die Arbeit mit MongoDB zu konvertieren.

Die Entwicklungs- und Produktteams von MongoDB haben aufmerksam auf das Feedback der Entwickler gehört, und MongoDB 8.0 wurde mit besonderem Augenmerk auf die Benutzerfreundlichkeit für Entwickler sowie auf Sicherheit, Haltbarkeit, Verfügbarkeit und Leistung entwickelt. Wir freuen uns, dass Sie es ausprobieren, und sind sicher, dass Sie die Leistungssteigerungen und anderen Vorteile von MongoDB 8.0 schätzen werden!

If you’re like me, over the past year you’ve closely watched AI’s developments—and the world’s reactions to them. From infectious excitement about AI’s capabilities, to impatience with its cost and return on investment, every day has been filled with AI twists and turns. It’s been quite the roller coaster.

During the ride, from time to time I’ve wondered where AI falls on the Gartner hype cycle, which gives "a view of how a technology or application will evolve over time." Have we hit the "peak of inflated expectations" only to fall into the "trough of disillusionment?" Or is the hype cycle an imperfect guide, as The Economist argues?

The reality is that it takes time for any new technology—even transformative ones like AI—to take hold. And every advance, no matter how big, has had its detractors. A famous example is that of Picasso (!), who in 1968 said, “Computers are useless. They can only give you answers.” (!!)

For our part, MongoDB is convinced that AI is a once-in-a-generation technology that will enhance every future application—a belief that has been reinforced by the incredible work our partners have shared at MongoDB’s 2024 events.

Speeding AI development

MongoDB is committed to helping organizations of all sizes succeed with AI, and one way we’re doing that is by collaborating with the MongoDB partner ecosystem to create powerful, user-friendly AI development tools and solutions.

For example, Fireworks.ai—which is a member of the MongoDB AI Applications Program ecosystem—created an inference solution that hosts gen AI models and supports containerized deployments. This tool makes it easier for developers to build and deploy powerful applications with a range of easy-to-use tools and customization options. They can choose to use state-of-the-art, open-source language, image, and multimodal foundation models off the shelf, or they can customize and fine-tune models to their needs.

Jointly, Fireworks.ai and MongoDB provide a solution for developers who want to leverage highly curated and optimized open-source models and combine these with their organization’s own proprietary data—and to do so with unparalleled speed and security.

“MongoDB is one of the most sophisticated database providers, and it’s very easy to use,” said Benny Chen, cofounder of Fireworks.ai. "We want developers to be able to use these tools, and we want to work with providers who enable and empower developers."

Nomic, another MAAP ecosystem member, also enables developers with best-in-class solutions across the entire unstructured data workflow. Their Embed offering, available through the Nomic API, allows users to vectorize large-scale datasets for use in text, image, and multimodal retrieval applications, including retrieval-augmented generation (RAG), using only their web browser.

The Nomic-MongoDB solution is a highly efficient, open-weight model that developers can use to visualize the unstructured datasets they store in MongoDB Atlas. These insights help users quickly discover trends and articulate data-driven value propositions. Nomic also supported the recently announced vector quantization in MongoDB Atlas Vector Search, which reduces vector sizes while preserving performance.

Last—but hardly least!—there’s our new reference architecture with MAAP partners AWS and Anthropic. Announced at MongoDB.local London, the reference architecture supports building memory-enhanced AI agents, and is designed to streamline complex processes and develop smarter, more responsive applications. For more—including a link to the code on Github—check out the MongoDB Developer Center.

Making AI work for anyone and everyone

The companies MongoDB partners with aren’t just making gen AI easier for developers—they’re building tools for everyone. For example, Capgemini has invested $2 billion in gen AI and is training 100,000 of its employees in the technology.

GenYoda, a solution that helps insurance professionals with their daily work, is a product of this investment. GenYoda leverages MongoDB Atlas Vector Search to analyze large amounts of customer data, like policy statements, premiums, claims history, and health information.

Using GenYoda, insurance professionals can quickly analyze underwriters’ reports to make informed decisions, create longitudinal health summaries, and streamline customer interactions to improve contact center efficiency. GenYoda can ingest 100,000 documents in just a few hours and respond to users’ queries in two to three seconds—a metric on par with the most widely used gen AI models.

And it produces results: in one example, by using Capgemini’s solution an insurer was able to increase productivity by 15%, add new reports 25% faster (thus speeding decision-making), and reduce the manual effort of searching PDFs, increasing efficiency by 10%.

Building the future of AI together

So, what’s next? Honestly, I’m as curious as you are. But I’m also incredibly excited. At MongoDB, we’re active participants in the AI revolution, working to embrace the possibilities that lie ahead. The future of gen AI is bright, and I can’t wait to see what we’ll build together.

MongoDB可查询加密(Queryable Encryption)由MongoDB加密研究小组(Cyptography Research Group)开发，是具有突破性意义的业界首创技术。该技术允许客户对应用中的敏感数据进行加密，不但可以将其以加密状态安全地存储在MongoDB数据库中，还可直接在加密数据上执行等值查询和范围查询，且无需具备加密专业知识。在原有可查询加密技术中增加了范围查询，进一步增强了数据检索功能，使搜索更加灵活和强大。目前，可查询加密在MongoDB Atlas、企业高级版（Enterprise Advanced）和社区版（Community Edition）中均可用。

覆盖数据安全全生命周期的加密技术

企业要确保对于敏感数据的保护并符合各种相关法律法规，如欧盟的《通用数据保护条例》 (GDPR)等，加密技术至关重要。这涉及将数据转换为任何没有解密密钥的人都无法读取的形式。加密可以通过三种方式保护数据：传输中（通过网络时）、静态（存储时）、使用中（处理期间）。传输中和静态数据的加密是所有数据库的标配(MongoDB也不例外)，但使用中的数据加密却带来了独特的挑战。

对于使用中数据的加密之所以困难，是因为加密后的数据不可读，看起来像是一串随机的字符和符号。传统上，数据库无法直接对加密数据进行查询，而必须先将其解密为可读形式。然而，如果数据库没有解密密钥，它就必须将加密数据发送回拥有密钥的应用程序或系统（如客户端），以便在查询之前进行解密。很显然，这种模式在实际应用中不具备扩展性。

这使企业陷入两难境地：对使用中数据的加密在数据隐私保护和法规合规性方面至关重要，但却难以实现。过去，公司要么选择不对使用中的敏感数据进行加密，要么采用安全性较低的变通方法，而后者会导致操作变得更加复杂。

MongoDB 可查询加密：保护使用中的数据，且不影响效率

MongoDB 可查询加密解决了这一难题。它允许组织对敏感数据（如个人身份、医疗信息等）进行加密，并能够在不解密的情况下直接对这些数据执行等值查询和范围查询。

可查询加密由MongoDB加密研究小组(Cyptography Research Group)开发，得益于团队成员所具备的密码学和加密搜索领域领先的专业知识，已经通过了全球顶尖密码学专家的同行评审。让MongoDB独特于业界其他厂商的是，MongoDB是目前唯一一个允许客户直接在非确定性加密数据上运行复杂查询的数据平台。客户可以因此获得突破性的优势，能够对敏感数据进行表达式查询，在不牺牲运营效率或开发者生产力的同时，为敏感数据提供强大的保护。

各行各业、各种规模的组织都能从可查询加密带来的显著成果中受益，例如：

• 数据保护更强：数据在传输、存储和使用中的每个阶段都保持加密状态，从而降低了敏感数据泄露或被攻破的风险。
• 增强法规合规性：通过确保数据在每个阶段都进行加密，为客户提供遵守如GDPR等数据保护法规所需的工具。
• 简化操作：无需昂贵的定制解决方案、专业的加密团队或复杂的第三方工具，即可简化加密过程。
• 明确的职责分离：支持更严格的访问控制，甚至MongoDB和客户自身的数据库管理员（DBA）都无法访问敏感数据。

MongoDB 可查询加密的使用场景

MongoDB 可查询加密可广泛用于各类需要对敏感数据进行保护的组织，不论其所在行业和规模大小。而且，可查询加密新增了对范围查询的支持，这进一步扩大了使用场景。以下为部分示例，用以说明可以如何使用可查询加密来保护和查询敏感数据：

• 金融服务
• 信用评分：通过查询加密数据（如信用评分和收入水平）来评估信用度。例如，根据信用评分在某个分数范围内的客户来进行客户细分。
• 欺诈检测：通过查询加密的交易金额，查找超越一般消费模式的异常值，如交易金额超过10万元的交易，以此来检测欺诈行为。
• 保险
• 风险评估：通过查询加密的客户数据，在指定范围内查找风险等级，从而个性化提供保险建议，提升客户服务质量，同时不披露敏感信息。
• 理赔处理：通过查询加密的理赔数据，查找金额在特定范围内或特定时间段内的理赔案件，实现理赔处理自动化，在简化操作流程的同时保护信息安全。
• 医疗健康
• 医学研究：对加密的医疗记录执行基于范围的搜索，例如查询特定年龄段内的患者或医学研究中异常的实验室结果的加密数据集。
• 账单和保险处理：对加密的账单数据执行安全的范围查询，以处理保险索赔和支付，同时保护患者的财务信息。
• 教育
• 评分系统：处理加密的学生分数，以在特定范围内评定分数等级，保护学生隐私并维护数据安全。
• 经济资助分配：分析特定范围内的加密收入数据，以确定学生是否符合奖学金、助学金的资格

保护数据安全生命周期的每一个环节

MongoDB可查询加密技术，可以为敏感数据在其整个生命周期内（无论是在传输、静态还是使用中）提供无与伦比的保护。现在，通过增加对范围查询的支持，MongoDB可查询加密技术更好地满足了现代应用程序的需求，并解锁了新的使用场景。

Today's rapidly evolving digital landscape is dominated by digital native companies, driving innovation. These are companies born in the digital age and who operate through digital channels with a business model enabled by technology and data. They are not only adept at using technology but are also reshaping the way software is developed and deployed.

This article delves into the challenges and opportunities facing digital natives in modern application development, with a particular focus on the complexities of managing data. We’ll explore how the right data platform can empower your digital native organization to build high-quality software faster, adapt to changing market demands, and unlock the full potential of your business.

Strong foundations: The four pillars of tech-fueled growth for digital natives

Achieving explosive growth requires a strong foundation built on specific principles, which empower rapid scaling and success. Here, we explore the four key pillars that fuel tech-driven growth for digital natives:

1. Product-market fit, fast: As a digital native, you must continuously ship and iterate products to achieve a quick product-market fit. This builds customer trust and captures opportunities before competitors can in an evolving market.

2. Data and AI-driven decisions: You must leverage data to personalize experiences, automate processes, and guide product decisions. A robust data architecture feeds real-time data into AI models, enabling data-driven decisions organization-wide.

3. Balance of freedom and control: Your developers must have the freedom to choose technologies, even as your organization maintains control over the infrastructure to manage risks and costs at scale. Selected technologies must integrate within your overall technology estate.

4. Extensible and open technologies: You must explore disruptive technologies while maintaining existing systems. Freedom from platform and vendor lock-in enables quick adoption of innovations, from current generative AI capabilities to future technological advances.

Data: The unsolved challenge in modern application development

From cloud platforms and managed services to gen AI code assistants, advancements have transformed how engineering teams build, ship, and run applications: Agile methods and programmatic APIs streamline development, while CI/CD and infrastructure as code automate processes. Containerization, microservices, and serverless architectures enable modularity, while new languages and frameworks boost capabilities. Enhanced logging and monitoring tools provide deep application health insights.

But none of these advancements address where developers spend most of their time—data. In fact, 73% of developers share time and again that working with data is the hardest part of building an application or feature. So why is data the problem?

Traditionally, selecting a database, often an open-source relational one, is the first step in development. However, these databases can struggle with the characteristics of modern data: it’s high volume, unstructured, and constantly evolving. As applications mature and their data demands grow, development teams may encounter challenges with achieving scalability and maintaining service resilience.

Some teams turn to NoSQL databases, but even then they find there are limited capabilities, pushing them back to relational databases.

As the application gains traction, the business’s appetite for innovation grows, compelling development teams to incorporate an expanding array of database technologies. This results in an architectural sprawl, imposing on teams the challenges of mastering, sustaining, and harmonizing new technologies. Concurrently, the dynamic technology landscape undergoes constant evolution, demanding teams to swiftly adjust. As a result, self-contained, autonomous teams encounter these hurdles recurrently, highlighting the pressing need for streamlined solutions to mitigate complexity and enhance agility.

Data sprawl: A major threat to developer productivity and business agility

Data sprawl is slowing everyone down. The more systems we add, the harder it is for developers to keep up. Each new database brings its own unique language, format, and way of working. This creates a huge headache for managing everything—from buying new systems to making sure they all work together securely. It’s a constant battle to keep data accessible, consistent, and backed up across all these different platforms.

It compromises every single one of the four outcomes your technology foundation should be providing, yielding the opposite results:

1. Missed opportunities, lost customers: Fragmented development experiences consume time as engineers struggle with multiple technologies, frameworks, and extract, transform, and load mechanisms for duplicating data between systems. This slows down releases, degrades digital product quality, and impedes engineers from achieving product-market fit and effective competition.

2. Flying blind: With your operational data siloed across multiple systems, you lack the data foundations necessary to use live data in shaping customer experiences or reacting to market changes. This is because you are unable to feed reliable, consistent, real-time data into your AI models to take action within the flow of the application or to provide the business with up-to-the-second visibility into operations.

3. High attrition, high costs: Complex data architecture impacts development team culture, leading to siloed knowledge, inefficient collaboration, and decreased developer satisfaction. This complexity also consumes substantial resources in maintaining existing systems by diverting resources from new projects that are vital for business competition in new markets.

4. Disruption from new technologies: Dependence on any one cloud provider can stifle innovation for development teams by restricting access to the latest technologies. Developers are confined to the tools and services offered by a single provider, hindering their ability to explore and integrate new, potentially more efficient, or advanced technologies.

Speed: A unified developer experience for building high-quality software faster

In today’s digital world, speed is king. Your customers expect seamless experiences, but clunky applications leave them frustrated. But traditional databases can be a bottleneck, struggling to keep pace with your ever-evolving data and slowing down development.

The future of data is here, and it’s flexible: a data platform built for digital natives. It leverages a flexible document model, letting you store and work with your data exactly how you need it. This eliminates rigid structures and complex migrations, freeing your developers to focus on what matters—building amazing applications faster.

• Flexible document data models empower developers to handle today’s rapidly evolving application data (80%+ unstructured) that relational databases struggle with.

• MongoDB documents are richly typed, boosting developer productivity by eliminating the need for lengthy schema migrations when implementing new features.

• Developers get to use their preferred tools and languages. Through its drivers and integrations, MongoDB supports all of the most popular programming languages, frameworks, integrated development environments, and AI-code assistance tools.

• MongoDB scales! It starts small and scales globally. Built for elasticity and horizontal scaling, it handles massive workloads without app changes.

Introducing MongoDB Atlas: a fully-managed cloud database built for the modern developer. It enables the integration of real-time data from devices with AI capabilities (through vector embeddings and large language models) to personalize user experiences. Stream processing empowers constant data analysis, while in-app analytics provides real-time insights without needing separate data warehouses, all while automatically managing data movement and storage for cost-effectiveness.

MongoDB Atlas simplifies database management with the following:

• Easy deployment via UI, API, CLI, Kubernetes, and infrastructure as code tools.

• Automated operations for cost-effective performance and real-time monitoring.

MongoDB Atlas customer success stories: Development with speed, scale, and efficiency

Delivery Hero

Delivery Hero, a global leader in online food delivery, leverages MongoDB Atlas to power its rapid service. Founded in 2011, Delivery Hero now serves millions of customers in over 70 countries through brands like PedidosYa, foodpanda, and Glovo.

Having replaced its legacy SQL database, Delivery Hero optimized operations and bolstered performance by using MongoDB Atlas. By leveraging MongoDB Atlas Search, Delivery Hero revolutionized its search functionality, ensuring a seamless user experience for its extensive customer base through simplified indexing and real-time data accuracy. MongoDB’s scalability has empowered Delivery Hero to manage over 100 million products in its catalog without encountering latency issues, enabling the company to expand its services while maintaining peak performance. This agility, coupled with MongoDB’s cost-effectiveness, has enabled Delivery Hero to swiftly adapt to evolving customer demands, solidifying its position in the fiercely competitive delivery market.

Read the full customer story to learn more.

Coinbase

Coinbase, a prominent cryptocurrency exchange boasting 245,000 ecosystem partners and managing assets worth $273 billion, trusts MongoDB to handle its extensive data workload. As the company grew, MongoDB scaled seamlessly to accommodate the increased demand. To further improve performance in the fast-paced crypto world, Coinbase partnered with MongoDB to develop a system that significantly accelerated data transfer to reporting tools, reducing processing time from days to a mere 5-6 hours. This near real-time data access enables Coinbase to rapidly analyze trends and make informed decisions, maintaining a competitive edge in the ever-evolving crypto landscape.

Watch Coinbase's full session at MongoDB.local Austin, 2024 to learn more.

MongoDB: Your flexible platform for digital growth

With MongoDB, you can freely explore, experiment, develop, and deploy according to your digital-native business needs.

If you would like to learn more about how MongoDB can empower your digital-native business to conquer market trends, visit:

• Innovate With AI: The Future Enterprise

• Application-Driven Intelligence: Defining the Next Wave of Modern Apps

• AI-Driven Real-Time Pricing with MongoDB and Vertex AI

It’s no surprise that AI is a topic of seemingly every professional conversation and meeting nowadays—my friends joke that 11 out of 10 words that come out of my mouth are “gen AI.” But an important question remains: do organizations truly know how to harness AI, or do they simply feel pressured to join the crowd? Are they driven by FOMO more than anything else?

One thing is for sure: adopting generative AI still presents a huge learning curve. Which is why we’ve been working to provide the right tools for companies to build innovative gen AI apps with, and why we offer organizations a variety of AI knowledge and guidance, regardless of where they are with gen AI.

We’re fortunate to work with our industry-leading partners to help educate and shape this nascent market. Working so closely with them on product launches, integrations, and solving real-world challenges allows us to bring diverse perspectives and a better understanding of AI to our customers, giving them the technology and confidence to move forward even before engaging with tough use cases and specific technical problems (something that the MongoDB AI Applications Program can definitely help with).

One of our main educational initiatives has been our webinar series with our top-tier MAAP partners. We’ve constantly launched video content to deepen understanding of topics essential to gen AI for enterprises answering broader questions such as “how can my company generate AI-driven outcomes” and “how can I modernize my workload,” to specific, tangible topics such as “how to build a chatbot that knows my business.” Each session is designed to move beyond the basics, sharing insights from experts in AI, and addressing our customers’ burning questions and challenges that matter most to them.

Welcoming new AI and tech partners

In October, we also welcomed four new AI and tech partners that offer product integrations with MongoDB. Read on to learn more about each great new partner!

Astronomer

Astronomer empowers data teams to bring mission-critical software, analytics, and AI to life and is the company behind Astro, the industry-leading data orchestration and observability platform powered by Apache Airflow.

"Astronomer's partnership with MongoDB is redefining RAG workflows for GenAI workloads. By integrating Astronomer's managed Apache Airflow platform with MongoDB Atlas' powerful vector database capabilities, we enable organizations to orchestrate complex data pipelines that fuel advanced AI and machine learning applications”, said Julian LaNeve, CTO at Astronomer. “This collaboration empowers data teams to manage real-time, high-dimensional data with ease, accelerating the journey from raw data to actionable insights and transforming how businesses harness the power of generative AI."

CloudZero

CloudZero is a cloud cost optimization platform that automates the collection, allocation, and analysis of cloud costs to identify savings opportunities and improve cloud efficiency rates.

"Database spending is one of the shared costs that can make it tricky for organizations to reach 100% cost allocation. CloudZero eliminates that problem," said Anand Sundaram, Senior Vice President of Product at CloudZero. “Our industry-leading allocation engine can organize MongoDB spend in a matter of hours, tracing it precisely to the products, features, customers, and/or teams responsible for it. This way, companies get a clear view of what’s driving their costs, who’s accountable, and how to optimize to maximize their cloud efficiency.”

ObjectBox

ObjectBox is an on-device vector database for mobile, IoT, and embedded devices that enables storing, syncing, and querying data locally online and offline.

"We’re thrilled to partner with MongoDB to give developers an edge,” celebrated Vivien Dollinger, CEO and co-founder of ObjectBox. “By combining MongoDB’s cloud and scalability with ObjectBox’s high-performance on-device database and data sync, we empower developers to build fast, data-rich applications that feel right at home across devices and environments. Offline, online, edge, cloud, whenever, wherever... We’re here to enable your data with speed and reliability."

Rasa

Rasa is a flexible framework for building conversational AI platforms that lets companies develop scalable generative AI assistants that hit the market faster.

“Rasa is excited to partner with MongoDB to empower companies in building conversational AI experiences. Together, we’re helping create generative AI assistants that save costs, speed up development, and maintain full brand control and security,” said Melissa Gordon, CEO of Rasa. “With MongoDB, deploying production-ready generative AI assistants is seamless, and we’re eager to continue accelerating our customers’ journey toward trusted conversational AI solutions.”

But wait, there's more!

Whether you’re starting out or scaling up, MongoDB and our partners are here with the resources, expertise, and trusted guidance to help you succeed in your genAI strategy! And if you have any suggestions for a good webinar topic, don’t hesitate to reach out.

To learn more about building AI-powered apps with MongoDB, check out our AI Resources Hub and stop by our Partner Ecosystem Catalog to read about our integrations with MongoDB’s ever-evolving AI partner ecosystem.

More retailers across ASEAN are looking to the document database model to support the expansion of their businesses and respond quickly to ever-more-rapidly changing customer demands.

Here are two stories shared during our MongoDB.local events in Indonesia and Malaysia in September 2024.

Simplicity and offline availability: EasyEat empowers merchants to optimize dining experiences with MongoDB Atlas

EasyEat delivers a software-as-a-service (SaaS) point-of-sale (POS) system tailored for restaurants. It simplifies daily operations, optimizes costs, and enhances customer satisfaction for merchants that provide food delivery and pickup services.

The platform launched in 2020, and in less than 4 years it has grown to serve over 1,300 merchants and over four million consumers across Malaysia and Indonesia.

Speaking at MongoDB.local Kuala Lumpur in September 2024, Deepanshu Rawat, Engineering Manager at EasyEat, explained how MongoDB Atlas empowered EasyEat to rapidly scale its operations across both the merchant POS and consumer applications.

EasyEat’s move from a SQL database to MongoDB Atlas also delivered greater flexibility, enabling faster product development and ease of use for the engineering team.

For EasyEat, MongoDB Atlas is more than just a database. The retailer is making full use of the developer data platform’s unique features, including:

• Analytics node: EasyEat must regularly provide reports to its merchants. These queries tend to be complex, taking significant time to process and putting an excessive load on the system.
  
  “With MongoDB Atlas’s analytics node, we are able to process those heavy queries without it impacting our daily operations,” said Rawat.

• Atlas Triggers: EasyEat uses this feature to perform a range of asynchronous operations.
  
  “Using Atlas Triggers helps us optimize the performance of our applications,” said Rawat.

• MongoDB Atlas Search: EasyEat has started using MongoDB Atlas Search to execute faster and more efficient searches as its platform’s user base grows.
  
  “Atlas Search enables us to make searches in our user application very smooth, and on our end, we don’t face any delay or latency issues,” said Rawat.

In addition, EasyEat is exploring a few other capabilities on MongoDB, including online archiving. The company is also considering how it can use generative AI via MongoDB Atlas Vector Search to build a personalized recommendations engine.

From 10 seconds to 1: Alfamart drives 1,000% efficiency using MongoDB Atlas

Alfamart is a leading retailer with over 19,000 stores across Indonesia and the Philippines. It serves 18.1 million customers and handles approximately 4.6 million retail transactions daily.

Speaking at MongoDB.local Jakarta in September 2024, Alfamart’s Chief Technology Officer, Bambang Setyawan Djojo, shared insights into how the company has used MongoDB Atlas to sustain massive scale and to power its digital transformation.

The 2015-2020 period was critical for Alfamart. It was in the midst of rapid expansion and had an ambitious digital transformation agenda.

In early 2020, as the COVID-19 pandemic began, Alfamart’s offline transactions plummeted while its online transactions soared.

“The growth of online transactions was not linear but exponential,” said Setyawan Djojo.

“This was the moment: We knew we needed the tools to adapt quickly and go to market fast. This is when we decided to look for a new database.”

With its previous SQL database, Alfamart struggled to handle the growing data load, particularly during peak hours.

MongoDB Atlas’s flexible document database model delivered greater efficiency for Alfamart’s team of 350 developers. It also smoothly accommodated Alfamart’s need for sudden and significant upscaling.

“Fast processing times are critical to keep our customers happy,” said Setyawan Djojo. “It used to take us 10 seconds to scan members during peak hours, but with MongoDB, it is now below one second.”

Setyawan Djojo added, “MongoDB helped us eliminate a lot of downtime compared to our previous SQL database.”

MongoDB Atlas’s auto-scaling capabilities were a game changer for Alfamart.

“MongoDB can automatically scale up and down depending on the usage of resources and performance. So during peak times, the database can scale up, and once the transaction peak is passed, it can scale back down,” said Setyawan Djojo.

Looking ahead, Alfamart plans to continue exploring the potential of the MongoDB Atlas platform to further increase productivity, efficiency, and flexibility.

We’re pleased to announce that MongoDB has been recognized as a Leader in the recently released Forrester Wave™: Translytical Data Platforms, Q4 2024.

The report—which highlights “Leaders, Strong Performers, Contenders, and Challengers” and is “an assessment of the top vendors in the market”—notes that “MongoDB is an excellent choice for organizations looking to enhance their document and NoSQL platforms with real-time insights by leveraging translytical capabilities.”

What are translytical capabilities?

So what are translytical capabilities? In short, modern applications use a growing number of data types for transactional, operational, and analytical uses. Developers can silo different data types and workloads into separate systems, but this causes architectural complexity and reduced agility for teams.

A better approach—and one that speeds development—is to leverage a single platform that can store and use multiple data types for different purposes. Forrester defines these “translytical data platforms” as “next-generation data solutions built on a single database engine to seamlessly support transactional, operational, and analytical workloads without compromising data integrity, performance, or real-time analytics.”

That’s why we built MongoDB Atlas as a developer data platform. It brings data like documents, vectors, streaming, and time-series together in one system so that you can run transactional, operational, and analytics workloads in one place.

How Forrester measured translytical capabilities

To measure providers, Forrester evaluated 15 of the most significant translytical data platform vendors against 26 criteria. These criteria span current offering and strategy, to market presence. Being recognized as a Leader is based on an organization’s scores in both current offering and strategy categories for criteria like vision and innovation.

Forrester gave MongoDB the highest possible scores across nine criteria, including:

• Multimodel¹

• Search

• Development Tools / API

• Scale optimization

• Streaming

• Platform management

• Roadmap

• Adoption

• Number of customers

According to the report, “MongoDB continues to expand its translytical market share by delivering new capabilities that enhance automation, intelligent memory tiering, and multimodel support, including vector, streaming, analytics, and integrated transactions.”

“Developers have been telling us for years that they need easy ways to work with all their data in one place,” said Jim Scharf, Chief Technology Officer at MongoDB. “That’s what continues to drive our strategy of making MongoDB Atlas the developer data platform. We’re excited to be recognized as a Leader in the new The Forrester Wave™: Translytical Data Platforms, and we will continue to support our customers’ growing needs for their data.”

What are MongoDB customers doing with translytical capabilities?

The Forrester report notes that organizations “use MongoDB to support real-time analytics, customer intelligence, the Internet of Things (IoT), and AI applications.” So, let’s look at a few examples in action.

Companies like Ignition started using MongoDB just for operational data—but, over time, expanded into using Atlas Vector Search for AI use cases. Meanwhile, Bosch Digital makes their IoT data easier to work with by bringing multiple data sources together in a single platform. And, Keller Williams uses MongoDB Charts to bring their analytics to where their transactional data is, making it faster to gather insights for their product teams.

Overall, customers are attracted to MongoDB because of how developer-friendly the platform is, and because it simplifies their lives by bringing their data together.

¹ Multimodel is defined as support for storing and using various data types.

As modern applications grow increasingly complex, developers face the challenge of meeting market demands for faster, smarter solutions. To stay ahead, they need tools that streamline their workflows, available directly in the environments where they build. According to the 2024 Stack Overflow Developer Survey, Microsoft’s Visual Studio Code (VS Code) is the integrated development environment (IDE) of choice for 74% of professional developers, serving as a central hub for building, testing, and deploying applications. With the rise of AI-powered tools like GitHub Copilot—which is used by 44% of professional developers—there’s a growing demand for intelligent assistance in the development process without disrupting flow.

At MongoDB, we believe that the future of development lies in democratizing the value of these experiences by incorporating domain-specific knowledge and capabilities directly into developer flows. That’s why we’re thrilled to announce the public preview of MongoDB’s extension to GitHub Copilot in VS Code. With this integration, developers can effortlessly generate MongoDB queries, inspect collection schemas, and get answers from the latest MongoDB docs—all without leaving their IDE.

But we’re not stopping there. As AI continues to evolve, so will the ways developers interact with their tools. Stay tuned for more exciting developments next week at Microsoft Ignite, where we’ll unveil more ways we’re pushing the boundaries of what’s possible with AI through MongoDB and Microsoft’s partnership!

What is MongoDB's Copilot extension?

MongoDB’s Copilot extension supercharges your GitHub Copilot in VS Code with MongoDB domain knowledge. The Copilot integration is built into the MongoDB for VS Code extension, which has more than 1.8M downloads in the VS Code marketplace today.

Type ‘@MongoDB’ in Copilot chat and take advantage of three transformative commands:

1. Generate queries from natural language (/query)—this generates accurate MongoDB queries by passing collection schema as context to Github Copilot

2. Query MongoDB documentation (/docs)—this answers any documentation questions using the latest MongoDB documentation through Retrieval-Augmented Generation (RAG)

3. Browse collection schema (/schema)—this provides schema information for any collection and is useful for data modeling with the Copilot extension.

Generate queries from natural language

This command transforms natural language prompts into MongoDB queries, leveraging your collection schema to produce precise, valid queries. It eliminates the need to manually write complex query syntax, and allows developers to quickly extract data without taking their focus away from building applications. Whether you run the query directly from the Copilot chat or refine it in a MongoDB playground file, we’ve sped up the query-building process by deeply integrating these capabilities into the existing flow of MongoDB VS Code extension.

Query MongoDB documentation

The /docs command answers MongoDB documentation-specific questions, complemented by direct links to the official documentation site. There’s no need to switch back and forth between your browser and your IDE; the Copilot extension calls out to the MongoDB Documentation Chatbot API that leverages retrieval-augmented generation technology to generate responses that are informed by the most recent version of the MongoDB documentation. In the near future, these questions will be smartly routed to documentation for the specific server version of the cluster you are connected to in the MongoDB VS Code extension.

Browse collection schema

The /schema command offers quick access to collection schemas, making it easier for developers to access and interact with their data model in real-time. This can be helpful in situations where developers are debugging with Copilot or just want to know valid field names while developing their applications. Developers can additionally export collection schemas into JSON files or ask follow-up questions directly to brainstorm data modeling techniques with the MongoDB Copilot extension.

On the Horizon

This is just the start of our work on MongoDB’s Copilot extension. As we continue to improve the experience with new features—like translating and testing queries to and from popular programming languages, and in-line query generation in Playgrounds—we remain focused on democratizing AI-driven workflows, empowering developers to access the tools and knowledge they need to build smarter, faster, and more efficiently, right within their existing environments.

This post is the final in a three-part series on leveraging database observability.

Welcome back to our series on Leveraging Database Observability! Our previous post showcased a real-world use case highlighting how MongoDB Atlas’s observability tools effectively tackle database performance challenges. Whether you’re a developer, DBA, or DevOps engineer, our mission is to empower you to harness the full potential of your data through our observability suite.

Integrating Atlas metrics with your central enterprise observability tools can simplify your operations. By seamlessly working with popular observability tools, our approach helps teams streamline workflows and enhance visibility across systems.

Integrating MongoDB Atlas with third-party monitoring tools

MongoDB’s developer data platform combines all essential data services for building modern applications within a unified experience. Our purpose-built observability tools for Atlas environments offer automatic monitoring and optimization, guiding diagnostics tailored specifically for MongoDB. Additionally, we extend Atlas metrics into your existing enterprise observability stack, enabling seamless integration without replacing your current tools. This creates a consolidated, single-pane view that unifies Atlas telemetry with other tech and application metrics, ensuring comprehensive visibility into both database and full-stack performance. This integration empowers you to monitor, receive alerts, and make data-driven decisions within your existing workflows, driving greater efficiency.

Below is a quick guide to modifying integration settings through the Atlas UI and the popular integrations we support:

1. Navigate to the Project Integrations page in Atlas.

2. Choose the organization and project you want to configure from the navigation bar.

3. On the Project Integrations page, select the third-party services you’d like to integrate.

4. Configure the chosen services with the required API keys and regions.

Critical integrations for your observability platform

With Atlas’s Datadog and Prometheus integrations, you can send critical MongoDB metrics to these platforms, empowering detailed, real-time monitoring. Through Datadog, you can track database operation counts, query efficiency, and resource usage, ideal for pinpointing bottlenecks and managing resources. Similarly, Prometheus enables you to monitor essential metrics like query times, connection rates, and memory usage, supporting flexible tracking of database health and performance. Both integrations facilitate proactive detection of issues, alert configuration for resource thresholds, and a cohesive view of Atlas data when visualized in Grafana.

Atlas’s integration with PagerDuty streamlines incident management by sending metrics like performance alerts, billing anomalies, and security events directly to PagerDuty. This integration records incidents automatically, notifies teams upon alerts, and supports two-way syncing, ensuring resolved alerts in Atlas are reflected in PagerDuty. It enables efficient incident response and resource allocation to maintain system stability.

With Atlas integrations for Microsoft Teams and Slack, you can route key metrics—such as query latency, disk usage, and throughput—to these channels for timely updates. Teams can use these insights for real-time performance monitoring, incident response, and collaboration. Notifications through these platforms ensure your team stays informed on database performance, storage health, and user activity changes as they occur.

Use case: Centralized observability with MongoDB Atlas, Datadog, and Slack

Let’s walk through a hypothetical scenario for ShopSmart, an e-commerce company that leverages MongoDB Atlas to manage its product catalog and customer data. As traffic surges, the DevOps team faces challenges in monitoring application performance and database health effectively.

To tackle these challenges, the team leverages MongoDB Atlas’ integration with Datadog and Slack, creating a powerful observability ecosystem.

1. Integrating MongoDB Atlas with Datadog: The team pushes key MongoDB Atlas metrics into Datadog, such as query performance, connection counts, and Atlas Vector Search metrics. With Datadog, they can visualize these metrics and correlate overall MongoDB performance with their other applications. Out-of-the-box monitors and dedicated dashboards allow the team to track metrics like throughput, average read/write latency, and current connections. This visibility helps pinpoint bottlenecks in real time, ensuring optimal database performance and improving overall application responsiveness.

2. Setting up alerts in Datadog: The team configures alerts for critical metrics like high query latency and increased error rates. When thresholds are breached, Datadog instantly notifies the team. This proactive approach allows the team to address potential performance issues before they impact customers.
3. Integrating Datadog with Slack: To ensure fast communication, alerts are sent directly to the dedicated Slack channel, “ShopSmart-Alerts.” This integration fosters seamless collaboration, enabling the team to discuss and resolve issues in real-time.

With these integrations, ShopSmart’s engineering team can monitor performance quickly and address issues efficiently. The unified observability approach enhances operational efficiency, improves the customer experience, and supports ShopSmart’s competitive edge in the e-commerce industry. By leveraging MongoDB Atlas, Datadog, and Slack, the team ensures scalable performance and drives continuous innovation.

Conclusion

MongoDB Atlas empowers developers and organizations to achieve unparalleled observability and control over their database environments. By seamlessly integrating with central enterprise observability tools, Atlas enhances your ability to monitor performance metrics and ensures you can do so within your existing workflows. This means you can focus on building modern applications confidently, knowing you have the insights and alerts necessary to maintain optimal performance. Embrace the power of MongoDB Atlas and transform your approach to database management—because your applications can thrive when your data is observable.

And that wraps up our Leveraging Database Observability series! We hope you learned something new and found value in these discussions.