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.

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Building Gen AI with MongoDB & AI Partners | October 2024

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.

November 11, 2024
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MongoDB, Microsoft Team Up to Enhance Copilot in VS Code

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. Our collaboration with MongoDB continues to bring powerful, integrated solutions to developers building the modern applications of the future. The new MongoDB extension for GitHub Copilot exemplifies a shared commitment to the developer experience, leveraging AI to ensure that workflows are optimized for developer productivity by keeping everything developers need within reach, without breaking their flow. Isidor Nikolic, Senior Product Manager for VS Code, Microsoft 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: Generate queries from natural language (/query) —this generates accurate MongoDB queries by passing collection schema as context to Github Copilot Query MongoDB documentation (/docs) —this answers any documentation questions using the latest MongoDB documentation through Retrieval-Augmented Generation (RAG) 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. Download MongoDB’s VS Code extension and enable the MongoDB chat experience to get started today.

November 13, 2024