MongoDB and Google Cloud continue to build on their partnership, with MongoDB enhancing Google Cloud with pay-as-you-go abilities, unified billing, and integrations with multiple different GC features, including BigQuery. And, when it comes to data architecture, BigQuery and MongoDB are two products that are better together.
Google BigQuery and MongoDB are better together
Google’s serverless data warehouse, BigQuery, was launched in 2011 with an aim to enhance business agility as their cloud-native data warehouse. BigQuery allows for fast queries that can uncover insights using familiar SQL.
When MongoDB is added to the database technology stack as a complementary technology, it enhances the breadth of capabilities for the developer across a variety of use cases, including the following four examples.
Combined impact of the Enterprise Data Warehouse and the Operational Data Store
BigQuery is best suited as an Enterprise Data Warehouse (EDW), meaning it is designed to optimize long-running analytics. MongoDB Atlas, on the other hand, is best suited as an Operational Data Store (ODS), designed to optimally support high throughput and highly concurrent real-time operational applications that demand random access to an entity’s data in native JSON.
This combination means that BigQuery and MongoDB are complementary technologies that can jointly deliver more value — each delivering on their strongest qualities. BigQuery excels at long-running queries, while Atlas handles the real-time operational application needs with thousands of concurrent sessions and millisecond response times.
Enriched end-customer experiences
BigQuery enables data scientists and analysts with machine learning (ML) models and BI tools for structured and semi-structured data at scale. For roles that need results with a turnaround time of a day or more, BigQuery is a strong tool for big data queries. With MongoDB Atlas, engineers and development teams can build applications faster and handle highly diverse schema, query, and update patterns, adapting to demanding user needs and competition. Atlas can also deliver the real-time or less than 24-hour queries that are necessary to keep your business operational. Additionally, data can easily move back and forth between the two platforms, creating a prime combination for running analytics on operational data.
Being able to unlock the full potential of your data across your organization means that everyone has the insight into the business metrics they need, when they need it. This allows quicker decision making, as well as stronger and more accurate reporting.
Extensibility to MongoDB Atlas features
On top of the value and synergy that can be realized by a BigQuery+Atlas combination, other Atlas features can help enhance the usefulness and sophistication of a data architecture, such as:
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Atlas Charts can be leveraged to create rich visualizations of any data stored within Atlas.
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Atlas Triggers and Alerts can apply database logic in response to events or on a predefined schedule.
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Atlas Search brings full-text search at scale to all data across MongoDB and BigQuery alike.
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Atlas Data Federation enables aggregating data across multiple data sources, such as Atlas clusters and HTTPS endpoints, and transforming it into analytical formats (e.g., Parquet).
This means you can not only access data in real-time, but you can also analyze it in a visual, user-friendly way. This functionality makes your data more actionable, allowing you not only to answer questions about your business data but also make better predictions and future adjustments based on it. Furthermore, being alerted to certain data-based events and triggering new actions based on that information means you can have your data working more efficiently for you, freeing up time to innovate and focus on core business competencies. Lastly, this approach simplifies your data lifecycle, so JSON data from various applications and endpoints can easily be transformed and consumed for rich analytics.
Deeper understanding of your customer
Businesses can use fully managed MongoDB Atlas to store customer 360 profiles. A 360-degree view of a customer allows businesses to track an individual customer’s journey across multiple channels, devices, purchases, and interactions, and improves customer satisfaction. With the combination of Atlas and BigQuery, businesses can also use compiled data — such as, transactional data, behavioral data, user profile and segmentations, and business analytics — to match user profiles with products and services using Artificial Intelligence (AI). Vertex AI, a managed machine learning platform, provides all the Google cloud services in one place to deploy and maintain AI models.
Being able to easily access a 360 view for each customer and have automation around their customer journey helps with customer engagement and loyalty by improving customer satisfaction and retention through personalization and targeted marketing communications. It also enables retailers to aggregate customer interactions across all channels and identify valuable new customers.
Google BigQuery and MongoDB Atlas in the real world
Current, a leading U.S. challenger bank, uses innovative approaches, services, and technologies to serve people overlooked by traditional banks, regardless of age or income level, to help improve their financial outcomes. To help create customer experiences that cannot exist in traditional systems, Current chose to leverage Google Cloud, including BigQuery, with MongoDB layering the platform to achieve their goals.
Are you a Google BiqQuery customer that is curious about how MongoDB Atlas can amplify your existing data warehouse or data lake architecture? Try MongoDB Atlas for free today and spin up your first workload in minutes.