MongoDB Atlas Integration with Ably Unlocks Real-time Capabilities

Francesco Baldissera
December 18, 2024

Enterprises across sectors increasingly realize that data, like time, doesn’t wait. Indeed, harnessing and synchronizing information in real time is the new currency of business agility. Enter the alliance between MongoDB and Ably—a partnership that has led to Ably's new database connector for MongoDB Atlas.

The new database connector provides a robust framework for businesses to create real-time, data-intensive applications that can provide top-notch user experiences thanks to an opinionated client SDK to be used on top of LiveSync, ensuring both data integrity and real-time consistency—without compromising your existing tech stack.

The synergy of MongoDB Atlas and Ably LiveSync

This new MongoDB Atlas-Ably integration tackles a fundamental challenge in modern application architecture: maintaining data consistency across distributed systems in real-time. MongoDB Atlas serves as the foundation—a flexible, scalable database service that adapts to the ebb and flow of data demands. Meanwhile, Ably LiveSync acts as the nervous system, ensuring that every change, every update, resonates instantly across the entire application ecosystem.

The Ably LiveSync database connector for MongoDB Atlas offers a transformative approach to real-time data management, combining unparalleled scalability with seamless synchronization. This solution effortlessly adapts to growing data volumes and expanding user bases, catering to businesses of all sizes—from agile startups to established enterprises. By rapidly conveying database changes to end-users, it ensures that all stakeholders operate from a single, up-to-date source of truth, fostering data consistency across the entire organization.

At its core, LiveSync is built with robust resilience in mind, featuring built-in failover mechanisms and connection recovery capabilities. This architecture provides businesses with the high availability they need to maintain continuous operations in today's always-on digital landscape. Moreover, by abstracting away the complexities of real-time infrastructure, LiveSync empowers developers to focus on creating features that drive business value. This focus on developer productivity, combined with its scalability and reliability, positions Ably LiveSync for MongoDB Atlas as a cornerstone technology for companies aiming to harness the power of real-time data synchronization.

Diagram depicting real-time data synchronization architecture from a MongoDB Atlas cluster to many client devices using Ably Database connector, pub-sub and client SDK
Figure 1: Ably real-time integration with MongoDB Atlas.

Industry transformation: A real-time revolution

This new integration has a number of implications across various sectors.

For example, in the banking and financial services sector, the MongoDB Atlas-Ably integration enables instantaneous fraud detection systems that can promptly react to potential threats. Live trading platforms benefit as well, seamlessly updating to reflect every market change as it happens. Banking applications are equally enhanced, with real-time updating of account balances and transactions, ensuring that users always have access to the most recent financial information.

In the retail industry, meanwhile, the integration facilitates real-time inventory management across both physical and online stores, ensuring that supply matches demand at all times. This capability supports dynamic pricing strategies that can adapt instantly to fluctuations in consumer interest, and it powers personalized shopping experiences with live product recommendations tailored to individual customer preferences.

Manufacturing and mobility sectors also see transformative benefits. With the capability for real-time monitoring of production lines, businesses can implement just-in-time manufacturing processes, streamlining operations and reducing waste. Real-time tracking of vehicles and assets enhances logistics efficiency, while predictive maintenance systems provide foresight into potential equipment failures, allowing for timely interventions.

The healthcare sector stands to gain significantly from this technology. Real-time patient monitoring systems offer healthcare providers immediate alerts, ensuring swift medical responses when necessary. Electronic health records receive seamless updates across multiple care settings, promoting coherent patient care. Efficient resource allocation is achieved through live tracking of hospital beds and equipment, optimizing hospital operations.

Insurance companies are not left out of this technological leap. The integration allows for dynamic risk assessment and pricing models that adapt in real-time, refining accuracy and responsiveness. Instant claim processing and status updates enhance customer satisfaction, while live tracking of insured assets facilitates more accurate underwriting and expedites the resolution of claims.

Finally, in telecommunications and media this integration promises buffer-free content delivery and streaming services, vastly improving the end-user experience. real-time network performance monitoring enables proactive issue resolution, maintaining service quality. Users can enjoy synchronized experiences across multiple devices and platforms, fostering seamless interaction with digital content.

Today's business imperative

As industries continue to evolve at a rapid pace, the integration of MongoDB Atlas and Ably LiveSync provides a compelling way for businesses to not only keep up but lead the real-time revolution. For IT decision-makers looking to put their organizations at the forefront of innovation, this integration turns static data into a dynamic driver of business growth and market leadership.

Access MongoDB Atlas and Ably LiveSync Resources and start your journey towards real-time innovation today.

Learn more about how MongoDB Atlas can power industry-specific solutions.

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Commerce at Scale: Zepto Reduces Latency by 40% With MongoDB

Zepto is one of the fastest-growing Indian startups and a pioneer in introducing quick commerce to India. Quick commerce, sometimes referred to as “Q-commerce” is a new, faster form of e-commerce promising ultra-quick deliveries, typically in less than one hour. Founded in July 2021, Zepto has revolutionized the Indian grocery delivery industry, offering users a choice of over 15,000 products with a promised 10-minute delivery. Since its launch, the company has rapidly expanded its operations, recording 20% monthly growth and achieving annualized sales of $1.5 billion by July 2024. Zepto’s order processing and delivery system is instrumental in meeting its promise to customers. Zepto’s system routes new orders to a “dark store,” where bleeding-edge assignment systems help pack orders in under 75 seconds. A proprietary navigation system ensures riders can then deliver these orders promptly. As Zepto expanded, its monolithic infrastructure, based on a relational SQL database, could not achieve the scalability and operational efficiency the company needed. Zepto changed the game by turning to MongoDB Atlas . Mayank Agarwal, Senior Architect at Zepto, shared the company’s journey with MongoDB during a presentation at MongoDB.local Bengaluru in September 2024 . “We had a big monolith. All the components were being powered by PostgreSQL and a few Redis clusters,” said Agarwal. “As our business was scaling, we were facing a lot of performance issues, as well as restrictions in terms of the velocity at which we wanted to operate.” Zepto’s legacy architecture posed four key issues: Performance bottlenecks: As Zepto grew, the need for complex database queries increased. These queries required multiple joins, which put a significant strain on the system, resulting in high CPU usage and an inability to provide customers and delivery partners with accurate data. Latency: Zepto needed its API response times to be fast. However, as the system grew, background processing tasks slowed down. This led to delays and caused the system to serve stale data to customers. A need for real-time analytics: Teams on the ground, such as packers and riders, required real-time insights on stock availability and performance metrics. Building an extract, transform, and load (ETL) pipeline for this was both time-consuming and resource-intensive. Increased data scaling requirements: Zepto’s data was growing exponentially. Managing it efficiently became increasingly difficult, especially when real-time archival and retrieval were required. MongoDB Atlas meets Zepto’s goals “We wanted to break our monolith into microservices and move to a NoSQL database . But we wanted to evaluate multiple databases,” said Agarwal. Zepto was looking for a document database that would let its team query data even when the documents were structured in a nested fashion. The team also needed queryability on array-based attributes or columns. MongoDB fulfilled both use cases. “Very optimally, we were able to do some [proofs of concept]. The queries were very performant, given the required indexes we had created, and that gave us confidence,” said Agarwal. “The biggest motivation factor was when we saw that MongoDB provides in-memory caching , which could address our huge Redis cluster that we couldn’t scale further.” Beyond scalability, MongoDB Atlas also provided high reliability and several built-in capabilities. That helped Zepto manage its infrastructure day to day, and create greater efficiencies for both its end users and its technical team. Speaking alongside Agarwal at MongoDB.local Bengaluru, Kshitij Singh, Technical Lead for Zepto, explained: “When we discovered MongoDB Atlas, we saw that there were a lot of built-in features like the MongoDB chat support , which gave us very qualitative insights whenever we faced any issues. That was an awesome experience for us.” Data archival , sharding support , and real-time analytic capabilities were also key in helping the Zepto team improve operational efficiencies. With MongoDB, Zepto was able to deploy new features more quickly. Data storage at the document level meant less management overhead and faster time to market for new capabilities. Furthermore, MongoDB’s archival feature made it easier for Zepto to manage large datasets. The feature also simplified the setup of secondary databases for ETL pipelines, reducing the heavy lifting for developers. “You go on the MongoDB Atlas platform and can configure archival in just one click,” said Singh. Zepto reduces latency, handles six times more traffic, and more The results of migrating to MongoDB Atlas were immediate and significant: Zepto saw a 40% reduction in latency for some of its most critical APIs, which directly improved the customer experience. Postmigration, Zepto’s infrastructure could handle six times more traffic than before, without any degradation in performance. This scalability enabled the company to continue its rapid growth without bottlenecks. Page load times improved by 14% , leading to higher conversion rates and increased sales. MongoDB’s support for analytical nodes helped Zepto segregate customer-facing workloads from internal queries. This ensured that customer performance was never compromised by internal reporting or analytics. “MongoDB is helping us grow our business exponentially,” said Agarwal at the end of his presentation. Visit our product page to learn more about MongoDB Atlas.

December 17, 2024
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Leveraging BigQuery JSON for Optimized MongoDB Dataflow Pipelines

We're delighted to introduce a major enhancement to our Google Cloud Dataflow templates for MongoDB Atlas. By enabling direct support for JSON data types, users can now seamlessly integrate their MongoDB Atlas data into BigQuery, eliminating the need for complex data transformations. This streamlined approach not only saves users time and resources, but it also empowers customers to unlock the full potential of their data through advanced data analytics and machine learning. Figure 1: JSON feature for user options on Dataflow Templates Limitations without JSON support Traditionally, Dataflow pipelines designed to handle MongoDB Atlas data often necessitate the transformation of data into JSON strings or flattening complex structures to a single level of nesting before loading into BigQuery. Although this approach is viable, it can result in several drawbacks: Increased latency: The multiple data conversions required can lead to increased latency and can significantly slow down the overall pipeline execution time. Higher operational costs: The extra data transformations and storage requirements associated with this approach can lead to increased operational costs. Reduced query performance: Flattening complex document structures in JSON String format can impact query performance and make it difficult to analyze nested data. So, what’s new? BigQuery's Native JSON format addresses these challenges by enabling users to directly load nested JSON data from MongoDB Atlas into BigQuery without any intermediate conversions. This approach offers numerous benefits: Reduced operating costs: By eliminating the need for additional data transformations, users can significantly reduce operational expenses, including those associated with infrastructure, storage, and compute resources. Enhanced query performance: BigQuery's optimized storage and query engine is designed to efficiently process data in Native JSON format, resulting in significantly faster query execution times and improved overall query performance. Improved data flexibility: users can easily query and analyze complex data structures, including nested and hierarchical data, without the need for time-consuming and error-prone flattening or normalization processes. A significant advantage of this pipeline lies in its ability to directly leverage BigQuery's powerful JSON functions on the MongoDB data loaded into BigQuery. This eliminates the need for a complex and time-consuming data transformation process. The JSON data within BigQuery can be queried and analyzed using standard BQML queries. Whether you prefer a streamlined cloud-based approach or a hands-on, customizable solution, the Dataflow pipeline can be deployed either through the Google Cloud console or by running the code from the github repository . Enabling data-driven decision-making To summarize, Google’s Dataflow template provides a flexible solution for transferring data from MongoDB to BigQuery. It can process entire collections or capture incremental changes using MongoDB's Change Stream functionality. The pipeline's output format can be customized to suit your specific needs. Whether you prefer a raw JSON representation or a flattened schema with individual fields, you can easily configure it through the userOption parameter. Additionally, data transformation can be performed during template execution using User-Defined Functions (UDFs). By adopting BigQuery Native JSON format in your Dataflow pipelines, you can significantly enhance the efficiency, performance, and cost-effectiveness of your data processing workflows. This powerful combination empowers you to extract valuable insights from your data and make data-driven decisions. Follow the Google Documentation to learn how to set up the Dataflow templates for MongoDB Atlas and BigQuery. Get started with MongoDB Atlas on Google Marketplace . Learn more about MongoDB Atlas on Google Cloud on our product page .

December 17, 2024