MongoDB Helps Asian Retailers Scale and Innovate at Speed

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.