Nick Bell

19 results

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

IntellectAI Unleashes AI at Scale With MongoDB

IntellectAI , a business unit of Intellect Design Arena , is a trailblazer in AI. Since 2019 the company has been using MongoDB to drive a number of innovative use cases in the banking, financial services, and insurance (BFSI) industry. For example, Intellect Design Arena’s broader insurance business has been using MongoDB Atlas as a foundation for its architecture. Atlas’s flexibility enables Intellect Design Arena to manage varied and constantly evolving datasets and increase operational performance. Building on this experience, the company looked at deepening its use of MongoDB Atlas’s unique AI and search capabilities for its new IntellectAI division. IntellectAI Partner and Chief Technology Officer Deepak Dastrala spoke on the MongoDB.local Mumbai stage in September 2024 . Dastrala shared how the company has built a powerful, scalable, and highly accurate AI platform-as-a-service offering, Purple Fabric , using MongoDB Atlas and Atlas Vector Search . Using AI to generate actionable compliance insights for clients Purple Fabric helps transform enterprise data into actionable AI insights and solutions by making data ready for retrieval-augmented generation (RAG). The platform collects and analyzes structured and unstructured enterprise data, policies, market data, regulatory information, and tacit knowledge to enable its AI Expert Agent System to achieve precise, goal-driven outcomes with accuracy and speed. A significant part of IntellectAI’s work involves assessing environmental, social, and governance (ESG) compliance. This requires companies to monitor diverse nonfinancial factors such as child labor practices, supply chain ethics, and biodiversity. “Historically, 80% to 85% of AI projects fail because people are still worried about the quality of the data. With Generative AI, which is often unstructured, this concern becomes even more significant,” said Deepak Dastrala. According to Deepak Dastrala, the challenge today is less about building AI tools than about operationalizing AI effectively. A prime example of this is IntellectAI’s work with one of the largest sovereign wealth funds in the world, which manages over $1.5 trillion across 9,000 companies. The fund sought to utilize AI for making responsible investment decisions based on millions of unique data points across those companies, including compliance, risk prediction, and impact assessment. This included processing both structured and unstructured data to enable the fund to make informed, real-time decisions. “We had to process almost 10 million documents in more than 30 different data formats—text and image—and correlate both structured and unstructured data to provide those particular hard-to-find insights,” said Dastrala. “We ingested hundreds of millions of vectors across these documents, and this is where we truly understood the power of MongoDB.” For example, by leveraging MongoDB's capabilities, including time series collections, IntellectAI simplifies the processing of unstructured and semi-structured data from companies' reports over various years, extracting key performance metrics and trends to enhance compliance insights. “MongoDB Atlas and Vector Search give us flexibility around the schema and how we can turn particular data into knowledge,” Dastrala said. For Dastrala, there are four unique advantages of working with MongoDB—particularly using MongoDB Atlas Vector Search—that other companies should consider when building long-term AI strategies: a unified data model, multimodality, dynamic data linking, and simplicity. “For me, the unified data model is a really big thing because a stand-alone vector database will not help you. The kind of data that you will continue to ingest will increase, and there are no limits. So whatever choices that you make, you need to make the choices from the long-term perspective,” said Dastrala. Delivering massive scale, driving more than 90% AI accuracy, and accelerating decision-making with MongoDB Before IntellectAI built this ESG capability, its client relied on subject matter experts, but they could examine only a limited number of companies and datasets and were unable to scale their investigation of portfolios or information. “If you want to do it at scale, you need proper enterprise support, and that’s where MongoDB became really handy for us. We are able to give 100% coverage and do what the ESG analysts were able to do for this organization almost a thousand times faster,” said Dastrala. Previously, analysts could examine only between 100 and 150 companies. With MongoDB Atlas and Atlas Vector Search, Purple Fabric can now process information from over 8,000 companies across the world, covering different languages and delivering more than 90% accuracy. “Generally, RAG will probably give you 80% to 85% accuracy. But in our case, we are talking about a fund deciding whether to invest billions or not in a company, so the accuracy should be 90% minimum,” said Dastrala. “What we are doing is not ‘simple search’; it is very contextual, and MongoDB helps us provide that high-dimension data.” Concluding the presentation speech on the MongoDB.local stage, Dastrala reminded the audience why IntellectAI is using MongoDB’s unique capabilities to support its long-term vision: “Multimodality is very important because today we are using text and images, but tomorrow we might use audio, video, and more. And don’t forget, from a developer perspective, how important it is to keep the simplicity and leverage all the options that MongoDB provides.” This is just the beginning for IntellectAI and its Purple Fabric platform. “Because we are doing more and more with greater accuracy, our customers have started giving us more problems to solve. And this is absolutely happening at a scale [that] is unprecedented,” said Dastrala. Using MongoDB Atlas to drive broader business benefits across Intellect Design The success encountered with the Purple Fabric platform is leading Intellect Design’s broader business to look at MongoDB Atlas for more use cases. Intellect Design is currently in the process of migrating more of its insurance and Wealth platforms onto MongoDB Atlas, as well as leveraging the product family to support the next phase of its app modernization strategy. Using MongoDB Atlas, Intellect Design aims to improve resilience, support scalable growth, decrease time to market, and enhance data insights. Head over to our product page to learn more about MongoDB Atlas . To learn more about how MongoDB Atlas Vector Search can help you build or deepen your AI and search capabilities, visit our Vector Search page .

December 12, 2024

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 .

December 10, 2024

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 .

November 25, 2024

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 .

November 12, 2024

Health-Tech Startup Aktivo Labs Scales Up With MongoDB Atlas

Aktivo Labs , a pioneering health-tech startup based in Singapore, has made significant strides in the fight against chronic diseases. Aktivo Labs develops innovative preventative healthcare technology solutions that encourage healthier lifestyles. The Aktivo Score ® —the flagship product of Aktivo Labs built on MongoDB Atlas —is a simple yet powerful tool designed to guide users toward healthier living. “By collecting and analyzing data from smartphones and wearables—including physical activity, sleep patterns, and sedentary behavior—the Aktivo Score provides personalized recommendations to help users improve their health,” said Aktivo Labs CTO Jonnie Avinash at MongoDB.local Singapore in August 2024 . Aktivo Labs also works closely with insurance companies. Acting as a data processor, it helps insurers integrate some of the Aktivo Score features into their own apps to improve customer engagement. Empowering insurers with out-of-the-box apps and user journeys From the start, the Aktivo Labs engineering team chose to work on MongoDB Atlas because the platform’s document model and cloud nature provided the flexibility and scalability required to support the company’s business model. The first goal of the engineering team was to enable insurance providers to integrate Aktivo Score smoothly within their own infrastructures. The team built software development kits (SDKs) that insurers can embed in various iOS and Android apps. The SDKs enable progressive web app journeys for user experience, which insurers can then rebrand and customize as their own. Next, the Aktivo Labs team created a web portal to help companies manage their apps and monitor their performance. This required discreet direct integrations with a myriad of wearables. “When we started to deploy things with companies, we were able to replicate this architecture so we could support all kinds of configurations,” Avinash said. “We could give you dedicated clusters if the number of users that you’re expecting is big enough. If you’re not expecting too many customers, we could give you colocated or shared environments.” Finding more efficiencies, flexibility, and scalability with MongoDB Atlas “When we started off, one of our challenges was that we had a very small engineering team. A lot of the focus had to be on functionality, and the cost of tech had to be kept low,” said Avinash. Working on MongoDB Atlas allowed the Aktivo Labs team to focus on product development rather than on database management and overhead costs. As the company grew and expanded to markets across Asia, Africa, and the Middle East, another challenge arose: Aktivo Labs needed to ensure its platform could scale and handle large volumes of disparate data efficiently. MongoDB Atlas was the optimal solution because its fully managed multi-cloud platform could easily scale as the company grew. MongoDB Atlas also provided Aktivo Labs the flexibility it needed to handle the wide variety, volume, and complexity of data generated by users’ health metrics. Based on insights from the MongoDB Atlas oplog, the engineering team made proactive updates to the database in real-time in anticipation of dynamic changes to leaderboards and challenges in the app. This approach enables Aktivo Labs to manage complex data flows efficiently, ensuring that users always have access to the latest metrics about their health. MongoDB Atlas’s secondary nodes and analytics nodes provide isolated environments for intensive data processing tasks, such as calculating risk scores for diabetes and hypertension. This separation ensures that the primary user-facing applications remain responsive, even during periods of heavy data processing. These isolated environments have also been an important factor in achieving compliance with the data-anonymization requirements from health insurers. “The moment you start showing that it’s a managed service and you’re able to show a lot of these things, the amount of faith that both auditors and clients have in us is a lot more,” said Avinash. Powered by MongoDB Atlas, Aktivo Labs is now looking to expand into U.S. and European markets, pursuing its mission of preventing chronic diseases on a global scale. Visit our product page to learn more about MongoDB Atlas.

October 29, 2024

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.

October 22, 2024

Grab Drives 50% Efficiencies with MongoDB Atlas

Grab is Southeast Asia’s leading ‘super application,’ offering a wide range of services, targeting both consumers and businesses, including deliveries, mobility, financial services, enterprise and more. Their range of applications, such as the popular Grab Taxi, Grab Pay, Grab Mart, Grab Ads, and more, count approximately 38 million active users monthly across 500 cities and eight countries. Managing a high volume of constantly growing users and handling regular spikes in demand and activity means that Grab needs to maintain a robust, scalable, and flexible digital infrastructure. Presenting at MongoDB.local Singapore in 2024, Grab shared their journey of migrating one of their key service apps— GrabKios —from the Community Edition of MongoDB to MongoDB Atlas . Grab also described how they are expanding their use of MongoDB to support semantic search. “Transitioning to MongoDB Atlas was not just a migration—it was a strategic move aimed at enhancing our database infrastructure,” said Jude Dulaj Lakshan De Croos, Database Engineering Manager at Grab. A smooth transition to MongoDB Atlas Grab’s journey with MongoDB Atlas began with the realization that their existing database infrastructure, while functional, was not equipped to handle the scale and complexity of their operations. Grab’s eventual migration to MongoDB Atlas was meticulously planned and executed, including extensive testing to ensure a smooth transition. During the critical testing phase, the creation of a replica “prod clone” environment, allowed Grab to test and refine their migration strategy. This minimized the possibility of unforeseen issues. The migration also involved the use of Mongomirror . This facilitated the seamless transfer of data from Grab’s self-hosted clusters to MongoDB Atlas. “We were able to ensure that migration was actually smooth and ran without any issues,” said Swarit Arora, Senior Database Engineer at Grab. MongoDB Atlas’s developer data platform offers Grab high levels of flexibility and scalability, accommodating Grab’s fast growth (the company recorded a 23% revenue growth YoY in 2024) in an ever-changing digital landscape. MongoDB Atlas also delivers unique automation and streamlining capabilities, as well as enterprise-grade support which led to improved process and database management efficiency. Efficiency gains with greater scalability, flexibility, performance MongoDB Atlas provided Grab with an automated, scalable, and secure platform, which empowered its engineering teams to focus on product development rather than database maintenance. “With MongoDB Atlas, we don’t have to worry about the scaling changes. And with hands-on security we can deliver secure and fast applications,” said Arora. “Being able to configure the exact resources required and then scale up and down based on our requirements is a plus. Considering we don't have to manage the scalability part, this is, I think, saving us around 50% of the time.” Furthermore, MongoDB Atlas delivers proactive recommendations to Grab’s team. For example, MongoDB Atlas’s Performance Advisor saves the team time by delivering real-time insights and recommendations to optimize query performance, ultimately reducing manual management tasks and increasing database efficiency. “It is now easy to set up our MongoDB clusters compared to what we were doing when we self-hosted, which was more time-consuming,” added Arora. “Secondly, if we are required to upgrade the cluster version, it is as easy as the click of a button.” Dedicated analytics nodes mean that Grab’s team is able to enhance the analytical capabilities of any application running on MongoDB. The successful migration to MongoDB Atlas has positioned Grab to explore new possibilities, including leveraging MongoDB’s advanced features for use cases such as semantic search and AI applications. Learn more about MongoDB Atlas .

October 14, 2024

THL Simplifies Architecture with MongoDB Atlas Search

Tourism Holdings Limited (THL) originally became a MongoDB customer in 2019, using MongoDB Atlas to help manage a wide variety of telematics data. I was very excited to welcome Charbel Abdo, Solutions Architect for THL at MongoDB .local Sydney in July 2024 to hear more about how the company has significantly expanded its use of MongoDB. The largest RV rental company in the world, THL has branches in New Zealand (where it is headquartered), Australia, the US, Canada, the UK and Europe. Specializing in building, renting, and selling camper vans, THL has a number of well-known brands under its umbrella. In recent years, THL has made a number of significant digital transformation and technology stack optimization efforts, moving from a ‘bolt-on’ approach that necessitated the use of a distributed search and analytics engine to an integrated search solution with MongoDB Atlas . THL operates a complex ecosystem managed by their in-house platform, Motek, which handles booking, pricing, fleet management, and more—with MongoDB Atlas as the central database. Its +7,000 RVs are fitted with telematics devices that send information—such as location, high-speed events, engine problems, and geofences or restricted areas (for example, during the Australian bushfires of 2020)—to vehicles’ onboard computers. THL initially used a bolt-on approach for complex search functionalities by extending their deployment footprint to include a stand-alone instance of Elasticsearch. This setup, while functional, introduced significant data synchronization and performance issues, as well as increased maintenance overhead. Elasticsearch struggled under heavy loads which led to critical failures and system instability, resulting in THL experiencing frequent outages and data inconsistencies. After two years of coping with these challenges, THL resolved to migrate away from ElasticSearch. After doing due diligence, they identified the MongoDB developer data platform’s integrated Search capabilities as the optimum solution. "A couple of months later, we had migrated everything," said Abdo. "Kudos to the MongoDB account team. They were exceptional." The migration process turned out to be relatively straightforward. By iteratively replacing Elasticsearch with MongoDB Atlas Search , THL was able to simplify its architecture, reduce costs, and eliminate the synchronization issues that had plagued the system. The simplification also led to significant performance and reliability improvements. Because it no longer needed the dedicated sync resources processing millions upon millions of records per day, THL was able to turn off its Elasticsearch cluster and to consolidate its resources. “All data sync related issues were gone, eliminated. But also we got our Friday afternoons back, which is always a good thing!” added Abdo. Abdo’s team can now also use existing monitoring tools rather than having to set up something completely separate from the standalone search engine they were using. “Sometimes, changes are easier than you think,” said Abdo. “We spent two-and-a-half years with our faulty solutions just looking for ways to patch up all the problems that we were having. We tried everything except actually looking into how much it would actually take to migrate. We wasted so much time, so much effort, so much money. While if we had thought about this a couple of years ago, it would have been a breeze.” “Over-engineering is bad, simple is better,” he noted. To learn more about how MongoDB Atlas Search can help you build or deepen your search capabilities, visit our MongoDB Atlas Search page .

October 7, 2024

Pathfinder Labs Tames Data Chaos and Unleashes AI with MongoDB

Pathfinder Labs develops software that specializes in empowering law enforcement agencies and investigators to apprehend criminals and rescue victims of child abuse. The New Zealand-headquartered company is staffed by professionals with diverse backgrounds and expertise, including counter-terrorism, online child abuse investigations, industrial espionage, digital forensics and more, spanning both the government and private sectors. Last July, I was thrilled to welcome Pathfinder Labs’ CEO Bree Atkinson, as well as co-founder and DevOps Architect, Peter Pilley to MongoDB .local Sydney where they shared more about the company’s innovative solutions powered by MongoDB. Those solutions are deployed and utilized by prestigious organizations on a global scale, including Interpol . Pathfinder Labs’ main product, Paradigm , has been built on MongoDB Atlas and runs on AWS . The tool—which relies on MongoDB’s developer data platform and document database model to sift through complex and continually growing numbers of data sets—helps collect, gather, and convert data into actionable decisions for law enforcement professionals. Pilley explained that Paradigm was “made by investigators, for investigators.” Paradigm is designed to present the information it helps gather in a way that will support a successful prosecution and outcome at trial. MongoDB Atlas enables Pathfinder Labs to tame the chaos arising from the data sets created and gathered throughout an investigation. MongoDB’s scalability and automation capabilities are particularly helpful in this regard. Powered by MongoDB Atlas, Paradigm can also easily identify similarities between cases, and uncover unique insights by bringing together information from disparate data sources. This could, for example, be about bringing together geolocalization data and metadata from an image, or identifying similar case patterns from law enforcement agencies operating in different states or countries. Ultimately, Paradigm simplifies evidence gathering and analysis, integrates external data sources and vendors, future-proof investigation methods, and helps minimize overall costs. Its capabilities are unlocking a whole new generation of data-driven investigative capabilities. During the presentation, Pilley used the example of the case of a missing female in the United States: it took a team of three investigators 12 months to solve the case. Using Paradigm, PathfinderLabs was able to solve that same case in less than an hour. “With Paradigm, we were able to feed some extra information and solve the case in 40 minutes. MongoDB Atlas allowed us to make quick decisions and present information to investigators in the most efficient way.” Pathfinder Labs also incorporates AI capabilities, including MongoDB Vector Search , which help identify which information is particularly relevant, select specific data points that can be used at a strategic point in time, connect data from one case to another, and identify what information might be missing. MongoDB Atlas Vector Search helps Pathfinder match images and details in images (i.e. people, objects), classify documents and text, and to build better search experiences for users via semantic search. “I was super excited when [Atlas Vector Search] came out. The fact that I can now have it as part of my standard workflow without having to deploy other kits all the time to support our vector searches has been an absolute game changer,” added Pilley. Finally, the team has seen great value in MongoDB’s Performance Adviser and Schema Anti Patterns features: “The performance Adviser alone has solved many problems,” concluded Pilley. To learn more and get started with MongoDB Vector Search, visit our Vector Search Quick Start page .

September 25, 2024

Building Modern Applications Faster: New Capabilities at MongoDB.local NYC 2024

Today, we kicked off MongoDB.local NYC and unveiled new capabilities across our developer data platform. The updates and capabilities announced today pave the way for a new era of app modernization and will allow developers to unleash the full potential of transformative technology like AI. Here’s an overview of our announcements, from a comprehensive update to MongoDB to AI-powered intelligent developer experiences: This post is also available in: Deutsch , Français , Español , Português , Italiano , 한국어 , 简体中文 . Modern applications need a modern database Cutting-edge modern applications must deliver both an exceptional experience and additional revenue. To meet these demands, developers require a database solution that offers optimal performance, scale, and operational resilience—while maintaining cost efficiency. So today, we’re thrilled to announce the preview of MongoDB 8.0 —the next evolution of MongoDB’s modern database. MongoDB 8.0 is focused on delivering unparalleled performance, scalability, security, and operational resilience to support the creation of next-generation applications, including sophisticated AI-driven solutions. It provides optimal performance by dramatically increasing query performance, improving resilience during periods of heavy load, making scalability easier and more cost-effective, and making time series collections faster and more efficient. Modernizing your next application with MongoDB is now easier As application modernization projects gain momentum, migrations are becoming a pressing reality for development and database teams. Transitioning from legacy relational systems to modern databases like MongoDB is essential to keeping up with technological shifts like AI. However, modernization and migrations have many challenges, from converting complex schemas and translating large amounts of application code to keeping databases in sync during long modernization projects. Announced in June 2023, MongoDB Relational Migrator streamlines the migration process by automating tasks like schema design, data migrations, and application code generation. Maintaining data synchronization is paramount in long-running modernization projects—where legacy relational databases must coexist with MongoDB until the project is complete. Today, we are pleased to announce that MongoDB Relational Migrator is now integrated with Confluent Cloud to support long-running change data capture (CDC) sync jobs. These jobs ensure operational resilience and observability, addressing the complexities of phased transitions without the added burden of managing Apache Kafka independently. Furthermore, migrating from legacy relational databases often involves significant effort in rewriting SQL queries, stored procedures, and triggers, which has traditionally been time-consuming and difficult. Now available in public preview, an AI-powered SQL Query Converter Tool has been introduced to MongoDB Relational Migrator that automates the process of converting existing SQL queries, stored procedures, and triggers to work with MongoDB in languages like JavaScript, Java, or C#. This streamlined approach—paired with MongoDB professional services—enables a simplified migration process that can scale effectively. Helping developers build faster with confidence on MongoDB We recognize the vital role that developers play in the success of every project, which is why we’re dedicated to making their MongoDB experience as seamless as possible. Frameworks are a great way for developers to boost productivity, improve code consistency and quality, and ultimately deliver code faster. For the C# developer community, we are pleased to announce that the MongoDB Provider for Entity Framework Core (EF Core) is now generally available . This allows C# developers building with EF Core to unlock the full power of MongoDB's developer data platform—while still using the EF Core APIs and design patterns they already know and love. And, recognizing the needs of the PHP community, we’re also proud to introduce the Laravel Aggregation Builder . This feature simplifies the process of building complex aggregation queries within Laravel, the most popular framework among PHP developers. By enhancing the integration of MongoDB with Laravel, we aim to boost productivity and ease the complexity of query operations, ensuring PHP developers can also enjoy an optimized development experience with MongoDB. Generating queries and visualizations with AI Since its initial release in 2015, MongoDB Compass has helped developers quickly build and debug queries and aggregations for their application code. Today, MongoDB Compass introduces an AI-powered, natural language query experience , making it even easier for developers to use MongoDB’s powerful Query API. Now generally available, this feature lets developers use natural language to generate executable MongoDB Query API syntax for everything from simple queries to sophisticated aggregations through an intelligent and guided experience. For example, a developer can input "Filter vacation rentals by location, group the remaining documents by number of bedrooms, and calculate the average nightly rental price," MongoDB Compass will suggest code to execute the stages of the aggregation pipeline. Data visualizations are a powerful way of understanding application data, and embedding charts into user-facing applications further enhances their utility and appeal to developers. However, creating visualizations is often hampered by the need for in-depth knowledge of the dataset and proficiency in using business intelligence tools—skills that many developers may not have. Now available in public preview, we introduced an easy-to-use visualization tool with generative AI capabilities in MongoDB Atlas Charts . Using natural language prompts, developers can easily render charts and build dashboards, making visualizing data and enriching their apps simple and fast. For example, developers can input ‘Show me the list of movies released in the last year sorted by genre,’ and MongoDB Atlas Charts will gather data and quickly generate the requested visualization. Today’s announcements underscore MongoDB’s commitment to helping developers innovate quickly and easily. For more about the MongoDB.local NYC 2024 updates, check out the product announcements page on our website.

May 2, 2024

Creación de aplicaciones modernas más rápido: nuevas capacidades en MongoDB.local NYC 2024

Hoy, iniciamos MongoDB.local NYC y revelamos nuevas capacidades en nuestra plataforma de datos para desarrolladores. Las actualizaciones y capacidades anunciadas hoy allanan el camino para una nueva era de modernización de aplicaciones y permitirán a los desarrolladores liberar todo el potencial de la tecnología transformadora como IA. Aquí hay una descripción general de nuestros anuncios, desde una actualización completa de MongoDB hasta experiencias de desarrollador inteligente impulsadas por IA: Las aplicaciones modernas necesitan una base de datos moderna Las aplicaciones modernas de vanguardia deben ofrecer tanto una experiencia excepcional como ingresos adicionales. Para satisfacer estas demandas, los desarrolladores necesitan una solución de base de datos que ofrezca un rendimiento, una escalabilidad y una resistencia operativa óptimos, manteniendo al mismo tiempo la rentabilidad. Por eso, hoy nos complace anunciar la versión preliminar de MongoDB 8.0 , la próxima evolución de la base de datos moderna de MongoDB. MongoDB 8.0 se centra en ofrecer un rendimiento, escalabilidad, seguridad y resiliencia operativa sin precedentes para respaldar la creación de aplicaciones de próxima generación, incluidas sofisticadas soluciones impulsadas por IA. Proporciona un rendimiento óptimo al aumentar drásticamente el rendimiento de las consultas, mejorar la resiliencia durante períodos de gran carga, hacer que la escalabilidad sea más fácil y rentable, y hacer que las collections de Time Series sean más rápidas y eficientes. Modernizar su próxima aplicación con MongoDB ahora es más fácil A medida que los proyectos de modernización de aplicaciones ganan impulso, las migraciones se están convirtiendo en una realidad apremiante tanto para los equipos de desarrollo como para los de bases de datos. La transición de sistemas relacionales heredados a bases de datos modernas, como MongoDB, es esencial para mantenerse al día con los cambios tecnológicos, como la IA. Sin embargo, la modernización y las migraciones tienen muchos desafíos, desde la conversión de esquemas complejos y la traducción de grandes cantidades de código de aplicación hasta el mantenimiento de las bases de datos sincronizadas durante los largos proyectos de modernización. Anunciado en junio de 2023, MongoDB Relational Migrator agiliza el proceso de migración mediante la automatización de tareas como el diseño de esquemas, las migraciones de datos y la generación de código de aplicaciones. Mantener la sincronización de datos es primordial en proyectos de modernización de larga duración, donde las relational databases heredadas deben coexistir con MongoDB hasta que se complete el proyecto. Hoy nos complace anunciar que MongoDB Relational Migrator ahora está integrado con Confluent Cloud para admitir trabajos de sincronización de captura de datos modificados (CDC) de larga duración. Estos trabajos garantizan resiliencia operativa y observabilidad, abordando las complejidades de las transiciones por fases sin la carga adicional de gestionar Apache Kafka de forma independiente. Además, la migración desde bases de datos relacionales heredadas a menudo implica un esfuerzo significativo para reescribir consultas SQL, procedimientos almacenados y desencadenadores, lo que tradicionalmente llevó mucho tiempo y fue difícil. Ahora disponible en versión preliminar al público, se introdujo en MongoDB Relational Migrator una herramienta de conversión de consultas SQL impulsada por IA que automatiza el proceso de conversión de consultas SQL, procedimientos almacenados y activadores existentes para trabajar con MongoDB en lenguajes como JavaScript, Java o C#. Este enfoque optimizado, combinado con los servicios profesionales de MongoDB, permite un proceso de migración simplificado que puede escalarse de manera efectiva. Cómo ayudar a los desarrolladores a crear más rápido y con confianza en MongoDB Reconocemos el papel vital que desempeñan los desarrolladores en el éxito de cada proyecto, por lo que nos dedicamos a hacer que su experiencia con MongoDB sea lo más fluida posible. Los marcos son una excelente manera para que los desarrolladores aumenten la productividad, mejoren la consistencia y calidad del código y, en última instancia, entreguen código más rápido. Para la comunidad de desarrolladores de C#, nos complace anunciar que MongoDB Provider for Entity Framework Core (EF Core) ya está disponible para el público general. Esto permite a los desarrolladores de C# que construyen con EF Core desbloquear todo el poder de la plataforma de datos para desarrolladores de MongoDB, sin dejar de utilizar las API de EF Core y los patrones de diseño que ya conocen y les encantan. Y reconociendo las necesidades de la comunidad PHP, también estamos orgullosos de presentar Laravel Aggregation Builder . Esta característica simplifica el proceso de creación de consultas de agregación complejas dentro de Laravel, el marco más popular entre los desarrolladores de PHP. Al mejorar la integración de MongoDB con Laravel, nuestro objetivo es aumentar la productividad y facilitar la complejidad de las operaciones de consulta, lo que asegura que los desarrolladores de PHP también puedan disfrutar de una experiencia de desarrollo optimizada con MongoDB. Generación de consultas y visualizaciones con IA Desde su versión inicial en 2015, MongoDB Compass ayudó a los desarrolladores a crear y depurar rápidamente consultas y agregaciones para el código de su aplicación. Hoy en día, MongoDB Compass presenta una experiencia de consulta de lenguaje natural impulsada por IA, lo que facilita aún más a los desarrolladores el uso de la potente Query API de MongoDB. Ahora disponible para el público general, esta característica permite a los desarrolladores usar lenguaje natural para generar la sintaxis ejecutable MongoDB Query API para todo, desde consultas simples hasta agregaciones sofisticadas a través de una experiencia inteligente y guiada. Por ejemplo, un desarrollador puede ingresar “Filtrar alquileres vacacionales por ubicación, agrupar los documentos restantes por número de habitaciones y calcular el precio promedio de alquiler nocturno”, MongoDB Compass sugerirá el código para ejecutar las etapas de la aggregation pipeline. Las visualizaciones de datos constituye una poderosa forma de entender los datos de las aplicaciones, y la incorporación de gráficos a las aplicaciones de cara al usuario aumenta aún más su utilidad y su atractivo para los desarrolladores. Sin embargo, la creación de visualizaciones a menudo se ve obstaculizada por la necesidad de un conocimiento profundo del conjunto de datos y la competencia en el uso de herramientas de inteligencia empresarial, habilidades que muchos desarrolladores pueden no tener. Ahora disponible en vista previa pública, presentamos una herramienta de visualización fácil de usar con IA generativa en MongoDB Atlas Charts . Con indicaciones en lenguaje natural, los desarrolladores pueden representar fácilmente gráficos y crear paneles, lo que simplifica y agiliza la visualización de datos y el enriquecimiento de sus aplicaciones. Por ejemplo, los desarrolladores pueden introducir “Muéstrame la lista de películas que se estrenaron en el último año ordenadas por género”, y MongoDB Atlas Charts recopilará los datos y generará rápidamente la visualización solicitada. Los anuncios de hoy subrayan el compromiso de MongoDB de ayudar a los desarrolladores a innovar de forma rápida y sencilla. Para obtener más información sobre las actualizaciones de MongoDB.local New York City 2024, consulte la página de anuncios de productos en nuestro sitio web.

May 2, 2024