Applications
Customer stories, use cases, and experiences of MongoDB
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.
Goodnotes Finds Marketplace Success Using MongoDB Atlas
In the fast-paced world of app development, creating a feature-rich digital marketplace that scales effectively can be challenging. Goodnotes was founded in 2010 with the aim of replacing traditional paper notebooks with a digital alternative that reimagines the note-taking experience. Since then, the app has gone through several iterations and grown in popularity, now with more than 24 million monthly active users and 2.5 billion notes. The team behind Goodnotes spoke at MongoDB.local Hong Kong in September 2024. They shared their journey of using MongoDB Atlas and MongoDB Atlas Search to build and run a comprehensive marketplace that expands the company’s offerings, catering to its growing number of content creators. “At the beginning of 2023, we launched a pop-up shop, which was a very simple version of the marketplace, to test the water, and we realized it got really popular,” said Xing Dai, Principal Backend Engineer at Goodnotes. The full Goodnotes Marketplace launched in August 2024 as a space where content creators can enhance their note-taking experience by purchasing additional content, such as planners, stickers, and textbooks. Building a robust digital marketplace with MongoDB Atlas “The first and the most difficult challenge [was] that we are a multiplatform app, and if you want to launch on multiple platforms, you need to support different app stores as well as [the] web,” said Dai. Using MongoDB Atlas, Dai’s team created a fully configurable marketplace that would be accessible on different mobile and desktop platforms and the web. The initial pop-up shop’s infrastructure consisted of a Payload content management system connected to a MongoDB database. However, building a full-fledged marketplace was more challenging. The architecture needed to be scalable and include search, ordering, and customization capabilities. “With [MongoDB] Atlas, it was really easy to add the in-app purchase and build the subscription infrastructure to manage the purchase workflow,” said Dai. The Goodnotes team introduced NestJS—a JavaScript API framework—to build client APIs. It then developed a user-friendly portal for the operations team and for creators who want to upload new products. Finally, the team built a full event-based data pipeline on top of MongoDB. “What’s nice is that everything on the marketplace is actually configurable in the backend,” said Dai. “We don’t need to do anything other than configuring what we need to store in the database, and the iOS client will connect it to the backend.” “When we want to extend the marketplace to other platforms, nothing needs to be changed,” Dai added. “We only need to configure different shops for different platforms.” This means that Goodnotes can easily make its marketplace available on different app platforms, such as Apple and Android, and on the web. Adding searches, charts, and soon AI As Goodnotes added more products to its marketplace, users had difficulty finding what they wanted. Despite having limited resources, the Goodnotes team endeavored to build a comprehensive search function. Using MongoDB Atlas Search and MongoDB Atlas Triggers , the team built a search function that would generate the search view collection by-products and attributes, combining them into one collection. The team then added an Atlas Search index for the search field with an API exposing the search. “We also added an auto-complete function, which is very similar to search, in the sense that we just had to create a function to generate aggregated collections, trigger it using [MongoDB] Atlas Triggers, and add the index and expose it in the marketplace,” said Dai. The search function is now popular among marketplace users, making it quick and easy for them to find what they are looking for. Goodnotes also regularly uses MongoDB Atlas Charts . For example, it creates charts showing how many products there are in the system over time. One of the key next steps for Goodnotes involves using generative AI to translate product descriptions and content into different languages (the app is currently available in 11 languages). The team also wants to introduce personalized recommendations for a more tailored experience for each user. Ending the MongoDB.local presentation, Dai said: “MongoDB made it very fast and easy to build the whole marketplace and our search feature on top of the database using [MongoDB] Atlas Search. The solution scales, and so far we haven’t had any performance issues.” Visit our product page to learn more about MongoDB Atlas .
Customer Service Expert Wati.io Scales Up on MongoDB
Wati.io is a software-as-a-service (SaaS) platform that empowers businesses to develop conversation-driven strategies to boost growth. Founded by CEO Ken Yeung in 2019, Wati started as a chatbot solution for large enterprises, such as banks and insurance companies. However, over time, Yeung and his team noticed a growing need among small and medium-sized businesses (SMBs) to manage customer conversations more effectively. To address this need, Wati used MongoDB Atlas and built a solution based on the WhatsApp Business API. It enables businesses to manage and personalize conversations with customers, automate responses, improve commerce functions, and enhance customer engagement. Speaking at MongoDB.local Hong Kong in September 2024, Yeung said, “The current solutions on the market today are not good enough. Especially for SMBs [that] don’t have the same level of resources as enterprises to deal with the number of conversations and messages that need to be handled every day.” Supporting scale: From MongoDB Community Edition to MongoDB Atlas “From the beginning, we relied on MongoDB to handle high volumes of messaging data and enable businesses to manage and scale their customer interactions efficiently,” said Yeung. Wati originally used MongoDB Community Edition , as the company saw the benefits of a NoSQL model from the beginning. As the company grew, it realized it needed a scalable infrastructure, so Wati transitioned to MongoDB Atlas. “When we started reaching the 2 billion record threshold, we started having some issues. Our system slowed down, and we were not able to scale it,” said Yeung. Atlas has now become an essential part of Wati’s infrastructure, helping the company store and process millions of messages each month for over 10,000 customers in 165 countries. “Transitioning to a new platform—MongoDB Atlas—seamlessly was critical because our messaging system needs to be on 24/7,” said Yeung. Wati collaborated closely with the MongoDB Professional Services and MongoDB Support teams, and in a few months it was able to rearchitect the deployment and data model for future growth and demand. The work included optimizing Wati’s database by breaking it down into clusters. Wati then focused on extracting connections, such as conversations, and dividing and categorizing data within the clusters—for example, qualifying data as cold or hot based on the read and write frequencies. This architecture underpins the platform’s core features, including automated customer engagement, lead qualification, and sales management. Deepening search capabilities with MongoDB Atlas Search For Wati’s customers, the ability to search through conversation histories and company documents to retrieve valuable information is a key function. This often requires searching through millions of records to rapidly find answers so that they can respond to customers in real-time. By using MongoDB Atlas Search , Wati improved its search capabilities, ultimately helping its business customers perform more advanced analytics and improve their customer service agents’ efficiency and customer reporting. “[MongoDB] Atlas Search is really helpful because we don’t have to do a lot of technical integration, and minimal programming is required,” said Yeung. Looking ahead: Using AI and integrating more channels Wati expects to continue collaborating with MongoDB to add more features to its platform and keep innovating at speed. The company is currently exploring to build more AI capabilities of Wati KnowBot , as well as how it can expand its integration with other conversation platforms and channels such as Instagram and Facebook. To learn more about MongoDB Atlas, visit our product page . To get started with MongoDB Atlas Search, visit the Atlas Search product page .
Hanabi Technologies Uses MongoDB to Power AI Assistant, Hana
For all the hype surrounding generative AI, cynics tend to view the few real-world implementations as little more than “fancy chatbots.” But for Abhinav Aggarwal, CEO of Hanabi Technologies , the idea of a generative AI-powered bot that is more than just an assistant was intriguing. “I’d been using ChatGPT since it launched,” said Aggarwal. “That got me thinking: How could we make a chatbot that was like a team member?” And with that concept, Hana was born. The problem with bots “Most generative AI chatbots do not act like people; they wait for a command and give a response,” said Aggarwal. “We wanted to create a human-like chatbot that would proactively help people based on what they wanted—automating reminders, for example, or fetching time zones from your calendar to correctly schedule meetings.” Hanabi’s flagship product, Hana, is an AI assistant designed to enhance team collaboration within Google Chat, working in concert with Google Workspace and its suite of products. “Our target customers are smaller companies of between 10 and 50 people. At this size you’re not going to build your own agent from scratch,” he said. Hana integrates with Google APIs to deliver a human-like assistant that chimes in with helpful interventions, such as automatically setting reminders and making sure meetings are booked in the right time zone for each participant. “Hana is designed to bring AI to smaller companies and help them collaborate in a space where they are already working—Google Workspace,” Aggarwal explained. The MongoDB Atlas solution For Hana to act like a member of the team, Hanabi needed to process massive amounts of data to support advanced features like retrieval-augmented generation (RAG) for better information retrieval across Google Docs and many other sources. And with a rapidly growing user base of over 600 organizations and 17,000+ installs, Hanabi also required a secure, scalable, and high-performing data storage solution. MongoDB Atlas provided a flexible document model, built-in vector database, and scalable cloud-based infrastructure, freeing Hanabi engineers to build new features for Hana rather than focusing on rote tasks like data extract, transform, and load processes or manual scaling and provisioning. Now, MongoDB Atlas handles a variety of responsibilities: Scalability and security: MongoDB Atlas’s auto-scaling and automatic backup features have enabled Hanabi to seamlessly grow its user base without the need for manual database management. RAG: MongoDB Atlas plays a critical role in Hana’s RAG functionality. The platform enables Hanabi to split Google Docs into small sections, create embeddings, and store these sections in Atlas’s vector database. Development Processes: According to Aggarwal, MongoDB’s flexibility in managing changing schemas has been essential to the company’s fast-paced development cycle. Data Visualization: Using MongoDB Atlas Charts has enabled Hanabi to create comprehensive dashboards for real-time data visualization. This has helped the team track usage, set reminders, and optimize performance without needing to build a manual dashboard. Impact and results With MongoDB Atlas, Hanabi can successfully scale Hana to meet the demands of its rapidly expanding user base. The integration is also enabling Hana to offer powerful features like automatic interactions with customers, advanced information retrieval from Google Docs, and manually added memory snippets, making it an essential tool for teams around the world. Next steps Hanabi plans to continue integrating more tools into Hana while expanding its reach to personal Gmail users. The company is also rolling out a new automatic-interaction feature, further enhancing Hana’s ability to proactively assist users without direct commands. MongoDB Atlas remains a key component of Hanabi’s stack, alongside Google Kubernetes Engine, NestJS, and LangChain, enabling Hanabi to focus on innovating to improve the customer experience. Tech Stack MongoDB Atlas Google Kubernetes Engine NestJS LangChain Are you building AI apps? Join the MongoDB AI Innovators Program today! Successful participants gain access to free MongoDB Atlas credits, technical enablement, and invaluable connections within the broader AI ecosystem. If your company is interested in being featured, we’d love to hear from you. Connect with us at ai_adopters@mongodb.com.
3 Ways MongoDB EA Azure Arc Certification Serves Customers
One reason more than 50,000 customers across industries choose MongoDB is the freedom to run anywhere—across major cloud providers, on-premises in data centers, and in hybrid deployments. This is why MongoDB is always working to meet customers where they are. For example, many customers choose MongoDB Atlas (which is available in more than 115 cloud regions across major cloud providers) for a fully managed experience. Other customers choose MongoDB Enterprise Advanced (EA) to self-manage their database deployments to meet specific on-premises or hybrid requirements. To that end, we’re pleased to announce that MongoDB EA is one of the first certified Microsoft Azure Arc-enabled Kubernetes applications, which provides customers even more choice of where and how they run MongoDB. Customer adoption of Azure Arc has grown by leaps and bounds. This new certification, and the launch of MongoDB EA as an Arc-enabled Kubernetes application on Azure Marketplace , means that more customers will be able to leverage the unparalleled security, availability, durability, and performance of MongoDB across environments with the centralized management of their Kubernetes deployments. We are very excited to have MongoDB available for our customers on the Azure Marketplace. By extending Azure Arc’s management capabilities to your MongoDB deployments, customers gain the benefit of centralized governance, enhanced security, and deeper insights into database performance. Azure Arc makes hybrid database management with MongoDB efficient and consistent. Collaboration between MongoDB and Microsoft represents an opportunity for many of our customers to further accelerate their digital transformation when building enterprise-class solutions with Azure Arc. Christa St Pierre, Partner Group Manager, Azure Edge Devices, Microsoft Here are three ways the launch of MongoDB EA on Azure Marketplace for Arc-enabled Kubernetes applications gives customers greater flexibility. 1. MongoDB EA supports multi-Kubernetes cluster deployments, simplifies management MongoDB Enterprise Advanced seamlessly integrates market-leading MongoDB capabilities along with robust enterprise support and tools for self-managed deployments at any scale. This powerful solution includes advanced automation, comprehensive auditing, strong authentication, reliable backup, and insightful monitoring capabilities, all of which work together to ensure security compliance and operational efficiency for organizations of any size. The relationship between MongoDB and Kubernetes is one of strong synergy. With Kubernetes, MongoDB EA really can run anywhere, such as a single deployment spanning on-premises and more than one public cloud Kubernetes cluster. Customers can use the MongoDB Enterprise Kubernetes Operator, a key component of MongoDB Enterprise Advanced, to simplify the management and automation of self-managed MongoDB deployments in Kubernetes. This includes tasks like creating and updating deployments, managing backups, and integrating with various Kubernetes services. The ability of the MongoDB Enterprise Kubernetes Operator to deploy and manage MongoDB deployments that span multiple Kubernetes clusters significantly enhances resilience, improves disaster recovery, and minimizes latency by allowing data to be co-located closer to where it is needed, ensuring optimal performance and reliability. 2. Azure Arc complements MongoDB EA, providing centralized management While MongoDB Enterprise Advanced is already among a select group of databases capable of operating across multiple Kubernetes clusters , it is now also supported in Azure Arc-enabled Kubernetes environments. Azure Arc enables the standardized management of Kubernetes clusters across various environments—including in Azure, on-premises, and even other clouds—while harnessing the power of Azure services. Azure Arc accomplishes this by extending the Azure control plane to standardize security and governance across a wide range of resources and locations. For instance, organizations can centrally monitor all of the Azure Arc-enabled Kubernetes clusters using Azure Monitor for containers , or they can enforce threat protection at scale using Microsoft Defender for Kubernetes. This centralized control significantly reduces the complexity of managing Kubernetes clusters running anywhere, as customers can oversee all resources and apply consistent security and compliance policies across their hybrid environment. 3. Customers can leverage the resilience of MongoDB EA and the centralized governance of Azure Arc Together, these solutions empower organizations to build robust applications across a wide array of environments, whether on-premises or in multi-cloud settings. The combination of MongoDB Enterprise Advanced and the MongoDB Enterprise Operator simplifies the deployment of MongoDB across Kubernetes clusters, allowing organizations to fully leverage enhanced resilience and geographic distribution that surpasses the capabilities of a single Kubernetes cluster. Azure Arc further enhances this synergy by providing centralized management for all of these Kubernetes clusters, regardless of where they are running; for customers running entirely in the public cloud, we recommend using MongoDB’s fully managed developer data platform, MongoDB Atlas. If you’re interested in learning more, we invite you to explore the Azure Marketplace listing for MongoDB Enterprise Advanced for Arc-enabled Kubernetes applications. Please note that aside from use for evaluation and development purposes, this offering requires the purchase of a MongoDB Enterprise Advanced subscription. For licensing inquiries, we encourage you to reach out to MongoDB at https://www.mongodb.com/contact to secure your license and to begin harnessing the full potential of these powerful solutions.
Accelerating MongoDB Migration to Azure with Microsoft Migration Factory
Migrating MongoDB workloads from on-premises solutions or other cloud platforms to MongoDB Atlas on Azure has never been simpler, thanks to Microsoft’s Cloud Migration Factory (CMF). This newly created program is perfect for organizations using MongoDB Enterprise Advanced or Community Edition who are ready to modernize. By transitioning to MongoDB Atlas —an integrated suite of data and applications services—customers can simplify their database management, enhance performance, and reduce operational complexities, unlocking new potential and value from their data. Why the Microsoft Cloud Migration Factory (CMF)? The Microsoft CMF offers hands-on delivery for eligible workloads to accelerate customer journeys on Azure at no cost. With repeatable best practices, robust tools, structured processes, and a skilled resource pool, the Microsoft CMF delivery model mitigates technical risk and accelerates deployments with optimized architectures to maximize platform benefits. The MongoDB Migration Factory, meanwhile, is a comprehensive program designed to help organizations migrate their existing databases to MongoDB. This program provides a structured approach, tools, and best practices to ensure a smooth and efficient migration process. Microsoft CMF is partnering with MongoDB Migration Factory to jointly deliver migrations of MongoDB Enterprise Advanced or Community Edition deployments to MongoDB Atlas on Azure in a secure, optimized, and customer-focused way. This comprehensive migration approach enables businesses to leverage Azure for their MongoDB-based solutions with speed, confidence, best practices, and minimal disruption risk at an optimized cost. “This joint delivery offering from Microsoft Cloud Migration Factory (CMF) and MongoDB Migration Factory is designed to accelerate AI transformation priorities for our customers by driving the migrations to MongoDB Atlas on Azure with speed and quality,” said Rashida Hodge, Corporate Vice President of Azure Data and AI at Microsoft. “We have delivered thousands of customer engagements with the CMF model across all Azure workloads, making it a proven approach for accelerating cloud journeys with Microsoft-owned delivery.” Why MongoDB Atlas on Azure? MongoDB Atlas on Azure combines MongoDB’s robust document data platform with Azure’s scalability and advanced cloud services, making it ideal for high-performance applications. Offering features like automatic scaling, high availability, and comprehensive security, MongoDB Atlas on Azure supports diverse workloads, including transaction processing, in-app analytics, and full-text search. Integrations with Azure services—including Azure Synapse Analytics, Microsoft Fabric, and Power BI—enhance MongoDB Atlas’s analytics and visualization capabilities, and compliance with standards like HIPAA and GDPR ensures data privacy, enabling organizations to focus on innovation in a secure, scalable environment. Figure 1: MongoDB Atlas on Azure Integrations ecosystem Migrating MongoDB Community Edition or Enterprise Advanced to MongoDB Atlas on Azure Migrating from MongoDB Community Edition or MongoDB Enterprise Advanced to MongoDB Atlas on Azure offers numerous benefits, including enhanced scalability, security, and operational efficiency. MongoDB Atlas is a fully managed, cloud-based solution that simplifies database management by handling tasks like automatic scaling, high availability, and data backup. Leveraging Azure’s infrastructure, Atlas provides integrated services such as Azure Active Directory for improved authentication and identity management, and global cloud coverage to reduce latency by deploying clusters closer to users. MongoDB Atlas on Azure also includes robust security features like encryption at rest and in transit, network isolation, and advanced access controls, meeting compliance standards. These features are often difficult to implement in a self-managed environment. Additionally, Atlas offers advanced monitoring and automated tuning tools for optimizing database performance and resource usage, helping to reduce costs over time. For organizations considering migration to MongoDB Atlas, Microsoft CMF offers end-to-end guidance, providing a clear roadmap for every stage of the migration process, from initial validation to post-migration testing. With flexible migration paths that cater to a range of needs, Microsoft CMF supports live migrations using tools like mongosync and offline migrations with MongoDB’s native tools, enabling everything from minimal-downtime transitions to complete re-hosting. Best of all, Microsoft CMF is a complimentary service, which means that organizations don’t need to worry about budgets and can focus on the transition to MongoDB Atlas on Azure. In collaboration with MongoDB Professional Services, the CSX team leveraged MongoDB and Microsoft Migration Factory to migrate a mission-critical railroad transportation app quickly and seamlessly with zero downtime. John Maio, Department Head, Enterprise Data & Analytics at CSX Getting started Microsoft CMF’s structured approach guides organizations through each critical milestone to ensure a smooth migration process. For those interested in migrating their MongoDB setup to Azure, contact MongoDB today to take advantage of this free migration opportunity and experience the ease of MongoDB Atlas on Azure with Microsoft CMF support.
MongoDB Database Observability: Integrating with Monitoring Tools
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: Navigate to the Project Integrations page in Atlas. Choose the organization and project you want to configure from the navigation bar. On the Project Integrations page, select the third-party services you’d like to integrate. 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. 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. 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. 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. Sign up for MongoDB Atlas , our cloud database service, to see database observability in action. To dive deeper and expand your knowledge, check out this learning byte for more insights on the MongoDB observability suite and how it can enhance your database performance.
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. Visit our solutions page to learn more about how MongoDB is helping retailers innovate worldwide. Check out our quick-start guide to get started with MongoDB Atlas Vector Search today. Visit our product page to learn more about MongoDB Atlas Search .
MongoDB: Powering Digital Natives
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: 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. 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. 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. 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. Figure 1: Tools and processes to maximize velocity. 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. Figure 2: The evolving tech landscape. 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. Figure 3: Teams building on separate stacks leads to data sprawl and manageability issues across the organization It compromises every single one of the four outcomes your technology foundation should be providing, yielding the opposite results: 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. 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. 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. 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. Figure 4: A unified developer experience, integrating all necessary data services for building sophisticated modern applications 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. MongoDB Atlas Search was a game changer. We ran a proof of concept and discovered how easy it is to use. We can index in one click, and because it’s a feature of MongoDB, we know data is always up-to-date and accurate. Andrii Hrachov, Principal Software Engineer, Delivery Hero 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
Gamuda Puts AI in Construction with MongoDB Atlas
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. To learn more and get started with MongoDB Vector Search, visit our Vector Search Quick Start page.
Empower Innovation in Insurance with MongoDB and Informatica
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. Figure 1: Everything you need to modernize the practice of master data management. 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 Given we already leverage the performance and scale of MongoDB Atlas within our cloud-native MDM SaaS solution and share a common focus on high-value, industry solutions, this partnership was a natural next step. Now, as a strategic MDM partner of MongoDB, we can help customers rapidly consolidate and sunset multiple legacy applications for cloud-native ones built on a trusted data foundation that fuels their mission-critical use cases. Rik Tamm-Daniels, VP of Strategic Ecosystems and Technology at Informatica 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. If you would like to discover more about how MongoDB and Informatica can help you on your modernization journey, take a look at the following resources: Unify data across the enterprise for a contextual 360-degree view and AI-powered insights with Informatica’s MDM solution Automating digital underwriting with machine learning Claim management using LLMs and vector search for RAG
Built With MongoDB: Buzzy Makes AI Application Development More Accessible
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. The Buzzy UX is highly streamlined so even non-technical users can leverage the power of AI in their apps. 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. Ready to start bringing your own app visions to life? Try Buzzy and start building your application in minutes for Free. To learn more and get started with MongoDB Vector Search, visit our Vector Search Quick Start guide .