mongodb-as-a-service

3004 results

Improving Omnichannel Ordering: BOPIS & Delivery with MongoDB

Today's customers expect a seamless shopping experience across both online and physical channels. The ability to Buy Online, Pick Up in Store (BOPIS)—or to receive deliveries at home—has become essential for meeting modern demands and staying competitive. BOPIS has surged around 40% since the start of the pandemic , according to McKinsey & Company, resulting in logistical savings for retailers. It also enables retailers to sell additional products and services in-store. What’s more, a study by Bain & Company shows that over 80% of shoppers who plan to use BOPIS expect to shop for additional items while picking up their online orders. As a result, retailers face the challenge of ensuring real-time inventory visibility, quick order fulfillment, and reliable delivery—all while managing data from multiple sources. With the right omnichannel ordering strategy, retailers can unify these touchpoints to offer customers a personalized and efficient shopping experience across all channels. The challenge of omnichannel ordering Omnichannel ordering bridges online and in-store interactions to create a smooth, unified journey for customers. However, many retailers are still working with outdated, disconnected systems that limit the customer experience. Today a customer may browse an item online only to find it unavailable in-store, or to face delays in delivery without tracking options. An effective omnichannel ordering experience would eliminate these inconveniences. A 2024 study by Uniform Market shows that around 73% of retail shoppers now interact with multiple channels, and companies that implement omnichannel solutions see revenue growth between 5 and 15 percent and improvements in cost to serve efficiencies of 3 to 7 percent , according to a 2023 paper by McKinsey & Company. So what’s holding retailers back from implementing an omnichannel solution? Much of it boils down to outdated infrastructure. In many cases, order data is spread across legacy systems, fragmented between point-of-sale software, ERP, and inventory management systems—which probably weren’t designed to work together seamlessly. These systems are often off-the-shelf, and lack the flexibility needed for modern integrations, meaning essential features like BOPIS or home delivery tracking can’t be supported without costly, time-consuming modifications. Often running on relational databases, legacy systems use rigid schemas that struggle to accommodate the dynamic, varied nature of omnichannel order data. Consequently, data silos prevent real-time inventory updates and cross-channel access, which are essential to an omnichannel strategy. Retailers increasingly recognize that building an omnichannel solution in-house with a modern database enables the flexibility and scalability they need to stay competitive. Not only does this improve control over the customer journey, but it also allows retailers to customize features tailored to their unique business needs. Without this shift, retailers are missing out on increased sales and loyalty, as fragmented systems leave customers with delays, unavailable items, or an impersonal experience, impacting both customer retention and brand reputation. Figure 1: Overview of how an omnichannel ordering solution works The customer browses the product catalog, which is updated in real-time by the inventory management system that is, in turn, updated by the distribution center and retail stores. Once the customer places an order, they select if they want it to be delivered or if they will pick up the order in-store. Order orchestration and order processing will act accordingly. In the end, the customer will pick up their order in-store or have it delivered at home, depending on the delivery choice. Excelling at omnichannel ordering A modern omnichannel ordering system integrates online and in-store channels for a seamless customer experience. Retailers are shifting to distributed, cloud-based architectures to enable real-time inventory and order tracking across all channels. They use microservices for flexibility, allowing each component (e.g., payments, inventory, shipping) to scale independently based on demand. The next natural step would include predictive analytics for demand forecasting, AI-driven personalization, and dynamic fulfillment options. This setup enables retailers to deliver faster, tailored, and frictionless shopping experiences, capitalizing on opportunities to drive customer loyalty and meet modern expectations. With a robust omnichannel ordering solution, retailers can address key challenges efficiently: Real-time inventory visibility: With accurate, real-time inventory updates across channels, retailers can prevent overselling and ensure customers have access to reliable stock information, critical for both BOPIS and delivery. Scalability during peak demand: The solution needs to be able to scale to manage spikes in traffic and transactions, especially during high-demand periods like holidays, preventing system overloads and downtime. To give an example , Commercetools delivered a 100% uptime to their customers during Black Friday and Cyber Monday in 2023. MongoDB underpins the Commercetools platform with a MACH-compliant, agile data platform built for real-time data, AI integration, rich product search, discovery, and other essential commerce and general features. Unified order management: Centralizing order data across all channels (online and in-store) enables retailers to manage and track orders seamlessly from a single platform, reducing errors and improving efficiency. Streamlined data management: Its schema flexibility adapts to changing data requirements without costly reconfigurations, making it easier to adjust to new sales channels or service offerings. Enhanced order tracking: Real-time processing supports end-to-end order tracking, keeping customers updated from purchase to fulfillment, which is crucial for delivery scenarios. Data privacy & security: Built-in security features, like encryption during all of the data lifecycle and access control, ensuring sensitive customer data is protected. How to begin Retailers can start with omnichannel ordering using MongoDB by first identifying key customer journeys, such as BOPIS and online deliveries. With these in mind, they can set up a central data platform, ensuring real-time data sync across inventory and customer touchpoints. Next, integrating with existing e-commerce, CRM, and ERP systems allows retailers to centralize and manage data seamlessly. MongoDB’s flexible schema makes it easy to unify diverse data types, such as order histories and location-specific inventories. Order data is especially well-suited to MongoDB's flexible document model because it often includes a variety of attributes that can change over time, such as product details, customer information, shipping options, and order status. With MongoDB, each order can be stored as a document, accommodating diverse fields and structures within the same database, making it easy to capture complex, nested data like item lists or personalized customer notes. Additionally, MongoDB’s schema flexibility allows retailers to add new fields, such as promotional codes or special instructions, without costly migrations or downtime. This adaptability makes it ideal for evolving order data requirements, ensuring scalability and smooth integration across different sales channels. Retailers can accelerate omnichannel ordering development with MongoDB by using its flexible document model. MongoDB’s seamless API integration connects inventory, customer, and order data across platforms, creating a unified experience. Additionally, MongoDB Atlas automates key tasks like scaling, allowing developers to focus on core features instead of infrastructure. With real-time data capabilities, retailers can quickly track and adjust order flows, enhancing the solution's responsiveness to customer needs. Figure 2: Retail OMS facilitates the end-to-end process of the order lifecycle, from placement to fulfillment, ensuring efficiency, accuracy, and customer satisfaction. What can you gain by using MongoDB Atlas? Implementing omnichannel ordering with MongoDB offers retailers significant value by enhancing both customer experience and operational efficiency. With real-time data synchronization, customers can see accurate inventory availability, making BOPIS and home delivery smoother and more reliable than ever before. MongoDB's scalability means retailers can handle peak shopping periods without compromising performance, ensuring seamless transactions even during high demand. Additionally, MongoDB's flexible, cloud-based architecture allows retailers to adapt quickly to new trends or channels, fostering innovation and helping them stay competitive in a fast-evolving market. With advantages like Real-Time Order Tracking MongoDB's distributed architecture supports live order updates, helping retailers keep customers informed from purchase to delivery, enhancing satisfaction, and reducing support inquiries. With MongoDB’s flexible schema, retailers can leverage order history data and preferences to deliver personalized recommendations and tailored promotions, increasing customer loyalty and repeat purchases. Ready to take a step into the omnichannel ordering world? Today, a robust omnichannel ordering system is no longer a luxury—it’s a necessity. By using MongoDB Atlas, retailers can ensure real-time inventory accuracy, scale effortlessly during peak times, and unify order data from multiple touchpoints and systems. Whether it's enabling the convenience of BOPIS or the flexibility of online deliveries, the solution’s distributed, agile database solution empowers retailers to meet and exceed customer expectations. As consumer behaviors and expectations continue to evolve, retailers leveraging MongoDB are well-positioned to adapt quickly, drive customer satisfaction, and stay ahead of the curve in a fast-paced market. Embracing MongoDB for omnichannel is a powerful step toward building a connected, efficient, and customer-centric retail experience. MongoDB’s agile data platform helps retailers manage complex omnichannel demands, improving both operational efficiency and customer satisfaction. Ready to transform your retail operations with a modern omnichannel solution? Discover how MongoDB Atlas can help you deliver seamless customer experiences across all channels.

January 9, 2025

Test Out Search Like Never Before: Introducing Search Demo Builder

MongoDB is excited to announce the availability of Search Demo Builder , the newest addition to the Atlas Search Playground. The Search Demo Builder allows anyone to jump right in and discover the value of MongoDB Atlas Search without first creating an Atlas account. The Search Demo Builder offers an intuitive environment for testing and configuring common search features—without having to build an index or to write queries from scratch. What is the Search Demo Builder? Search Demo Builder is an interactive tool within the Atlas Search Playground that makes exploring MongoDB Atlas Search simple and accessible. It allows you to explore, configure, and experiment with key features like searchable fields, autocomplete, and facets—all without needing technical expertise, writing queries, or building indexes from scratch. Best of all, with Search Demo Builder you can see exactly how changes affect the search results through the Search Experience Preview. This feature gives you a real-time look at what your experience would look like as you tweak and configure your feature set. Some of the key features of Search Demo Builder include: Searchable fields utilizing dynamic fields as the default, but with the option to specify fields to search against. Autocomplete that can be configured on string fields to enable a search-as-you-type experience, and includes index definition and autocomplete query. Filters and facets that are interactive and can be configured on arrays of strings and numbers. Experience preview screen where features are reflected in an interactive preview experience. Index and query definitions that are auto-generated based on the configured search features Figure 1:   A view of the new Search Demo Builder experience. User benefits associated with Search Demo Builder include: Instant setup: Start immediately with preloaded datasets or upload your own small collection—no sign-up or complex configuration required. Guided exploration: Step-by-step product tours and tooltips make Search Demo Builder accessible for users of all skill levels. Interactive workspace: Experiment with features like autocomplete and facets in a dedicated, visual environment. Shareable indexes and queries: View and copy generated indexes and query definitions for use outside of Search Demo Builder. Search Demo Builder versus Code Sandbox The Search Demo Builder is designed to make Atlas Search accessible for users who prefer a visual interface and makes exploring and testing search features quick. The Code Sandbox , meanwhile, offers deeper customization and hands-on experimentation with JSON queries. Together, these tools provide a comprehensive environment for working with Atlas Search, regardless of your experience level. For more information on the Atlas Search Playground, including the Code Sandbox, check out our initial announcement blog . Get started with Search Demo Builder today Ready to try out Atlas Search for yourself? Head over to Search Demo Builder today and see what you can do with Atlas Search (you can also navigate to it in the lefthand navigation once you visit the Atlas Search Playground UI). Whether you’re testing out ideas for a new project or just getting your feet wet, the new Search Demo Builder provides an easy to navigate experience that makes getting started a breeze. Figure 2: &nbsp Lefthand nav panel with Search Demo Builder. To learn more about the Atlas Search Playground, visit our documentation . And be sure to share what you think in our user feedback portal .

January 8, 2025

SonyLIV Improves CMS Performance By 98% On MongoDB Atlas

As one of the world's leading technology and media companies, Sony needs little introduction. Founded in 1954, Sony’s portfolio spans game & network services, music, pictures, entertainment technology & services, imaging & sensing solutions, financial services, and more. SonyLIV Technology , the digital arm of Sony Pictures Networks , has a strong footprint in India where it operates a leading over-the-top (OTT) video-streaming platform. OTT platforms deliver streamed content via internet-connected devices, a popular way of consuming content in India. A core part of SonyLIV’s operations is built on MongoDB Atlas . OTTs platforms handle massive amounts of datasets across video, audi, and text formats; this is only expected to keep growing as the number of OTT video users in India is set to reach 634.3 million by 2029 . As a result, a strong content management system (CMS) is central to ensuring users can easily discover and receive new recommended content, while also facilitating a smooth, enjoyable viewing experience. At MongoDB.local Bengaluru in September 2024 , Sumon Mal, Vice President of Backend Engineering at SonyLIV, described how the company built a new CMS platform—‘Blitz’— using MongoDB’s Node.js SDK and React Native SDK . Blitz hosts 495,000 documents that need to be easily accessible and editable by SonyLIV’s team, as well as by end-users. MongoDB’s flexible document model was chosen because it could handle that scale, as well as handle the large, dynamic video files that OTT businesses are built on. The challenge Before transitioning to MongoDB Atlas, SonyLIV relied on a legacy relational database, which posed four key challenges: Poor searchability: The content stored in the relational database was not easily searchable. This was detrimental to and compromised the end-user experience. Operational overhead: The rigid structure of the relational database hindered the engineering team from adapting quickly to dynamic and evolving data requirements. Complex maintenance: Managing and maintaining the database was a complex, time-consuming task. The rigid data model from the legacy database was slowing down development speed and time to market. Slow content updates: Due to the lack of bulk processing capabilities, publishing new content or updating existing videos took a significant amount of time—up to half a day each. This delay hindered SonyLIV’s ability to rapidly respond to content demands or push new updates to their users. “This was a business risk,” said Sumon. “These [challenges] pushed [us] to go for the modernization of this particular tech stack.” The first step of this modernization was to relaunch SonyLIV’s streaming platform on Amazon Web Services (AWS) . However, the project required converting 60,000 hours of video into multiple output formats and scaling to support more than 1.6 million simultaneous users. SonyLIV’s legacy relational database was unable to handle that sort of scale. The company’s new CMS platform could not meet the increased demand unless it had more power and flexibility. Migration to MongoDB Atlas: improved performance and lowered search query latency by 98% SonyLIV chose to build Blitz on top of MongoDB Atlas and to migrate SonyLIV’s decade-old data. Concurrently, the engineering team started publishing all of its new content via the new CMS underpinned by the MongoDB Atlas technology. Suman’s team was able to work on both fronts, uploading and publishing new content while the old data was being migrated. Suman also highlighted the importance of working closely with the MongoDB Professional Services team to unlock the full power of the document model and the Atlas platform in a way that would meet SonyLIV’s specific needs. For example, during the development phase, MongoDB Professional Services helped identify opportunities to optimize the new stack, such as API latency. Operations such as searching for data took up to 1.3 seconds. MongoDB’s Professional Services team immediately determined this was below anticipated response times and recommended an alternative approach that yielded immediate results. “I know very well how, as a developer, we think we will go read some blogs, YouTube videos nowadays, AI solutions. But the best way to do it is to ask the subject matter experts. So the MongoDB Professional Services team helped us to optimize it,” said Suman. Improving performance with MongoDB Atlas Search Suman and his team worked closely with the MongoDB Professional Services to improve index optimization and workload isolation as the number of data sets MongoDB Atlas needed to process increased. “One of the problems was our overall collection size and the capabilities in terms of the indexes,” said Suman. “Day by day, we are increasing the amount of new content that is getting published (thousands of pieces of content being added every single day). And on top of that, we have the decade-old data.” Out of 5 lakh [500,000], close to 2.7 lakh [270,000] documents were archived in SonyLiv’s legacy system. These documents were moved to online archiving on MongoDB Atlas . “Now, if you take any other database [...] you literally have to shift your data to somewhere else for archival; you don MongoDB Atlas’ Online Archive feature enabled SonyLIV to segregate data, which in turn improves performance greatly. Additionally, datasets are more precise and respond much faster, including while employing multiple indexes. SonyLIV also shifted toward using MongoDB Atlas Search to optimize the performance caused by $regex queries (sequences of characters used to search and locate specific sequences of characters that match a pattern). The team created an Atlas Search index on the collection. The native full-text search capabilities simplified the architecture and improved performance. The latency went from 1.3 seconds to 0.022 to 0.030 seconds, a 98% performance gain. This resulted in a flexible, high-performance CMS that reduces time-to-market and enhances user experience. The system now handles over 500,000 content items and supports real-time updates with minimal latency. The key takeaway from this story is the outcomes that can be derived from combining MongoDB Atlas’ powerful technology with the unique expertise from our teams on the ground. This is what can accelerate customers’ projects, help them unlock more value out of the platform, and ultimately bring flawless customer experiences to the world, faster. However, we should not underestimate the value MongoDB’s team of experts can bring. Ultimately, it is about helping customers use the technology as effectively as possible, and derive the greatest impact from the MongoDB Atlas platform. “If there is a black swan event and if I call [MongoDB subject matter expert], I know he will respond, and his team will be there to support me. I don't need to worry,” said Sumon. “Our collaboration goes further, and we optimize the overall MongoDB case to build our application [...], and behind the scenes empower all the content seamlessly publishing every single day.” Learn more about MongoDB Atlas on our product page. Get started with MongoDB Atlas Search today by visiting our product page to learn more.

January 8, 2025

知乎携手MongoDB为企业数据的安全可靠性保驾护航

数据是企业的生命线，数据库的稳定性是企业业务稳定的基座，保障数据库稳定运行对于企业数据的安全性和可靠性至关重要。 知乎，中文互联网高质量的问答社区和创作者聚集的原创内容平台，于2011年1月正式上线，以“让人们更好的分享知识、经验和见解，找到自己的解答”为品牌使命。知乎从问答起步，在过去的13年中逐步成长为一个大规模的综合性内容平台。 在高并发场景下，如何提升写入吞吐量，保障在线实时业务的稳定运行？ 随着数据规模的持续增长，如何有效地存储、管理和分析数据成为每个企业面临的重大挑战。作为数据管理的核心技术，数据库选型至关重要。在高并发的场景下，数据库的读写请求非常频繁，如果所有请求都集中到一个节点上，势必会导致性能瓶颈。随着用户访问量的急剧上升，如果数据库不能及时响应请求，将直接影响用户体验。 例如，知乎的核心业务场景——反作弊业务。这个业务场景通常需要实时对接，并及时地处理作弊内容和用户，迅速分析作弊原因并制定解决策略。作为一套用于反作弊的风控类业务，知乎反作弊业务要求有极高的并发写入吞吐量，甚至超过已读服务（根据资料显示，在2019年，知乎已读服务在高峰时间每秒写入超过4万条记录，日新增记录近30亿条）；同时，索引的构建还会严重影响写入吞吐量。在高并发写入场景下，优化数据库的写入性能和事务处理效率是非常重要的一环。 为了满足在海量数据存储环境下，可伸缩、高吞吐、高并发、高可用以及毫秒级数据实时性等方面的要求，知乎选用MongoDB支撑底层数据，解决了安全业务中的数据量大、低延迟、表结构不稳定以及JSON数据处理等问题。 良好的架构设计，让业务更加流畅 数据库架构设计的质量直接影响着数据库系统的性能、可靠性和可扩展性。一个良好的数据库架构设计能够提高数据库系统的响应速度、降低数据冗余、保证数据的一致性和完整性，并且能够适应未来的业务增长和变化。通过使用MongoDB集群架构模式，知乎在安全反作弊业务中设置了副本集和分片集群上下衔接的方式，能够根据不同的业务场景需要去选择适合的集群，从而实现了业务审核的及时处理和内部分析。 在任何数据库中，索引是提升查询效能的必要手段。MongoDB提供了丰富的索引类型和功能，并支持对数据的复杂访问模式。 知乎通过MongoDB创建了符合自身需求的组合索引，从而有效减少了在线实时业务的低延迟。同时，MongoDB创建索引都是在后台执行的，避免了对线上业务产生不良影响。随着业务时时刻刻发生新变化，MongoDB还能够提供定期分析和优化索引，避免过度索引。代晓磊谈到，“有了这个索引优化，只要业务没有发生太大的变化，再结合一些参数调优，其实业务还是跑得挺好的。” 数据库的稳定性建设不可或缺 提高数据库的稳定性是确保数据安全、服务可靠和业务连续性的关键。在知乎的安全反作弊业务场景中，每天要承载很多来自业务请求的突发流量，可能导致大流量副本集抖动。问题一旦出现，对业务端来说会直接导致审核变慢，很快就会招致业务部门投诉。 虽然知乎数据库的监控项非常多，每个集群有多种监控项，但每个集群的负载不同，它触发的报警阈值也不同。在同一套报警触发阈值下，不同负载的集群可能会给监控人员频繁地发送预警信息。知乎通过MongoDB 监控平台，有效解决了集群情况监控的难题。 MongoDB提供了全面的监控功能，可以对实例各节点资源的运行情况进行监控，通过监控每个MongoDB数据库中的索引、文件、对象和集合，跟踪每个元素的内存和存储消耗，并相应地优化资源。此外，MongoDB提供的细颗粒度报警，对于运维人员而言，也是相当友好的。 如何确保数据库的可扩展性？ 当大规模的数据存储和处理需求呈现出爆炸式增长态势时，这使得数据库的可扩展性成为了一个重要的考量因素。分片（Sharding）是一种跨多台机器分布数据的方法，通常都是使用分片来支持具有非常大的数据集和高吞吐量操作的部署。对于知乎而言，选取合适的分片均衡策略尤其重要。 MongoDB提供了两种思路：其一，通过划分区域（zone），把相关业务查询的近期数据全部集中放在一个分片中，避免在分片上汇聚数据，减轻数据库压力；其二，根据业务方反馈的字段规则，自动按年月分配标签（tag）并合理规划zone分配，最终实现自动化维护。 此外，依托于MongoDB压缩引擎，减少了磁盘空间的使用。经过测试，当未压缩数据为4.8T时，默认压缩方式snappy，占本地磁盘3.1T，压缩比例为64%；而测试压缩方式zstd，占本地磁盘1.9T，压缩比例为39%。显然，zstd压缩比例更好，最终帮助知乎将数据的使用空间压缩到一个合理的范围之内。 目前MongoDB不仅应用在知乎的安全反作弊业务场景中，还广泛应用在一些社区及其他业务线的部分集群中。代晓磊谈到，“未来，除了在更多场景中落地，我们还将继续通过MongoDB实现数据治理，通过云服务、多活能力、平台化建设进行版本升级，实现降本增效。” 点击了解更多MongoDB信息

January 8, 2025

Accelerating Sybase-to-MongoDB Modernization With PeerAI

The IT landscape has evolved dramatically over the past decade. Cloud-native architectures, advanced analytics, and AI have reshaped the way businesses use data. But the key requirements for these modern database systems—such as horizontal scalability, real-time insights, and support for AI workloads—are often beyond the capabilities of legacy platforms like Sybase Adaptive Server Enterprise (Sybase ASE). And with SAP announcing the end of life of this platform, organizations relying on it now face a critical decision. Document databases like MongoDB have emerged as transformative alternatives, offering unmatched flexibility and speed. However, migrating from Sybase to MongoDB is far from a lift-and-shift process—it requires a comprehensive transformation of both the data and application layers. This is where PeerAI, a platform from PeerIslands , can aid organizations in their modernization journeys. The evolution of Sybase and the need for change In the 1980s, Sybase emerged as a pioneering relational database, driving innovations in enterprise data management. Its integration into SAP’s HANA ecosystem in 2010 solidified its role as a cornerstone of legacy enterprise systems. However, SAP has announced the end of life for Sybase ASE after 2025. As many enterprises prepare to migrate, the shift in modern technology has led them to reevaluate their database strategies. And while moving from Sybase to another relational database may seem like the easiest option, such an approach often falls short of delivering the scalability, performance, and adaptability needed to meet modern business demands.. MongoDB Atlas , a fully managed cloud database, stands out as a preferred choice for organizations looking to modernize. With its developer-friendly document model, horizontal scalability, and seamless integration with major cloud providers, MongoDB empowers enterprises to unlock new possibilities. The complexity of Sybase-to-MongoDB modernization Migrating from Sybase to MongoDB is a journey that demands thoughtful planning and execution. Legacy systems like Sybase were designed for an era of predictable workloads and monolithic architectures, which struggle to keep pace with today’s real-time, data-intensive demands. The transition involves more than simply replacing one database with another. It requires a complete rethinking of architectures, workflows, and data models. Key challenges include: Legacy complexity: Decades-old systems often harbor deeply intertwined data and application layers. Extracting and restructuring these requires precision. High costs: Modernization demands up-front investment in resources and tools. Without a clear strategy, costs can quickly escalate. Lengthy timelines: Traditional migrations often take years, requiring businesses to support old and new systems simultaneously. Skills gaps: Expertise in legacy systems is limited, and finding skilled professionals for modern platforms like MongoDB adds to the challenge. Validation difficulties: Ensuring the new environment replicates or improves on the functionality of the legacy system requires extensive testing. Outdated methods: Conventional tools and approaches for relational-to-relational migrations are ill-suited for transitioning to MongoDB’s document-based model. Despite these challenges, modernization offers immense potential to not only overcome the limitations of legacy systems but also unlock new capabilities. Simplified Migration to MongoDB with PeerAI To address these complexities, PeerIslands developed PeerAI, a platform that simplifies and accelerates the migration process. Combining generative AI (gen AI) with the expertise of seasoned developers, PeerAI transforms modernization into a seamless journey. The process begins with a detailed code-and-database analysis of the Sybase environment. PeerAI uses AI-driven tools to map dependencies, schemas, and business logic, providing a comprehensive understanding of the system. This ensures that no critical functionality is overlooked during migration. Figure 1: Footprint analysis of database and application artifacts, part 1. Figure 2: Footprint analysis of database and application artifacts, part 2. PeerAI then automates the generation of domain models and microservice architectures tailored for MongoDB’s document model. It refactors legacy code, such as stored procedures and in-line functions, into efficient, modern frameworks. The platform also validates the migrated system, generating test suites to compare performance and functionality with the legacy setup. Figure 3: Legacy and target domain model. Figure 4: Generation of modernized code. Figure 5: Accelerated timeline for modernization using PeerAI. A real-world transformation: Global-bank case study A leading global bank faced the end-of-life for its Sybase ASE system, which included 10 application tables, 4 reference tables, and 22 stored procedures. Initially considering Amazon Aurora PostgreSQL, the bank found Aurora’s tooling insufficient for migrating stored procedures and maintaining functionality. Turning to MongoDB and PeerIslands, the bank embarked on a modernization journey using PeerAI. The platform completed the following steps: Conducted a deep analysis of the Sybase environment, mapping out dependencies and workflows Designed a MongoDB schema optimized for scalability and performance Refactored stored procedures into a Java / Spring Data JPA–based architecture Validated the migration using AI-generated test cases, ensuring the new system exceeded legacy performance Migrated data seamlessly, achieving zero downtime and ensuring alignment with the bank’s operational needs The results were transformative. PeerAI reduced migration timelines by 75%, enabling the bank to quickly transition to a future-ready MongoDB environment. Beyond addressing the immediate challenge of Sybase’s end of life, the modernization unlocked new opportunities for real-time analytics, scalability, and innovation. The key benefits of PeerAI By automating critical steps in the migration process, PeerAI delivers tangible benefits: Faster timelines: Traditional modernization projects take 12–18 months. PeerAI reduces this to just 3–4 months. Cost savings: Automation reduces manual effort, lowering overall project costs by up to 50%. Reduced risk: Comprehensive testing ensures the new system meets performance and reliability standards. Future-ready architecture: MongoDB’s flexible, scalable platform positions businesses for long-term success. A streamlined migration journey with PeerAI Modernizing legacy Sybase systems is no longer a choice but a necessity for organizations seeking to thrive in a data-driven world. With MongoDB and PeerIslands’ PeerAI, businesses can navigate this transformation efficiently and confidently. PeerAI turns what was once a lengthy, costly process into a streamlined journey, helping organizations transition to modern, cloud-native platforms with less risk and greater rewards. By embracing modernization, businesses not only address immediate challenges but also unlock the potential to innovate and grow in a rapidly changing digital landscape. The future of data management is here, and it’s powered by MongoDB and PeerAI. PeerIslands has joined the MongoDB AI Application Program (MAAP) to accelerate gen AI application development for organizations at any stage of their AI journeys. Visit the MAAP page to learn how ecosystem partners like PeerIslands can help your organization reduce time-to-market, lower risks, and maximize the value of your AI investments.

January 7, 2025

MongoDB Named a Leader in the 2024 Gartner® Magic Quadrant™ for Cloud Database Management Systems

I’m pleased to announce that MongoDB has been named a Leader in the 2024 Gartner® Magic Quadrant™ for Cloud Database Management Systems (DBMSs) for the third consecutive year. In our view, this recognition cements MongoDB’s status as the only pure-play database provider in the cloud database management system category, underscoring MongoDB’s innovation, execution, and customer-centric approach. According to Gartner, “The cloud DBMS market remains as vibrant as ever and is transforming in important ways, especially in the use of gen AI and how DBMSs interact with other data management components. This Magic Quadrant will help data and analytics leaders make the right cloud DBMS choices in this essential market.” We believe this continued recognition by Gartner is a testament to MongoDB’s commitment to serving developers, as well as the investments we’ve made in our unified platform and integrated services. Driving innovation for enterprises MongoDB's mission is to empower innovators to create, transform, and disrupt industries by unleashing the power of software and data. 2024 was a year of innovation and accolades at MongoDB, and I’m proud to share some of its highlights: In October, we released MongoDB 8.0 , the best performing version of MongoDB yet. MongoDB 8.0 is over 30% faster than the previous version of the database, it’s more secure than ever, horizontal scaling is faster and easier (at a lower cost), and MongoDB 8.0 gives teams greater control for optimizing database performance. We also launched—and grew—the MongoDB AI Applications Program (MAAP) . With MAAP, MongoDB offers customers a full AI stack and an integrated set of professional services to help them keep pace with innovation, identify the best AI use cases, and to help them future-proof AI investments. MongoDB became a founding member of the U.S. Artificial Intelligence Safety Institute Consortium . Established by the U.S. Department of Commerce’s National Institute of Standards and Technology, the Consortium supports the development and deployment of safe and trustworthy AI. MongoDB released hundreds of features and enhancements to accelerate innovation, manage costs, and simplify building applications at scale. MongoDB was recognized as the most loved vector database in Retool’s State of AI report —for the second consecutive year. The Gartner Magic Quadrant for cloud database management systems “Gartner defines the cloud database management systems (DBMSs) market as solutions designed to store, manipulate, and persist data, primarily delivered as Software-as-a-Service (SaaS). These systems must support transactional, analytical, and hybrid workloads while enabling enterprises to innovate across multi-cloud, hybrid, and intercloud ecosystems.” 1 It’s our opinion that this recognition by Gartner is a testament to MongoDB’s strong ability to execute and support customers today, as well as MongoDB’s comprehensive product vision that positions our platform to support tomorrow's operational workloads. What is the Magic Quadrant, and what is a Leader? “A Gartner Magic Quadrant is a culmination of research in a specific market, giving you a wide-angle view of the relative positions of the market’s competitors.  By applying a graphical treatment and a uniform set of evaluation criteria, a Magic Quadrant helps you quickly ascertain how well technology providers are executing their stated visions and how well they are performing against Gartner’s market view.” 2 According to Gartner, “Leaders execute well against their current vision and are well positioned for tomorrow.” Overall, Magic Quadrants can help you “get quickly educated about a market’s competing technology providers and their ability to deliver on what end-users require now and in the future.” Powering innovation at scale with MongoDB Atlas Enterprises choose MongoDB Atlas because it gives them the freedom and agility they need to succeed in a rapidly evolving digital landscape. MongoDB Atlas’s multi-cloud architecture—including availability across Amazon Web Services, Google Cloud, and Microsoft Azure—ensures customers can design for unmatched scale and resilience. By automating functions like scaling and performance optimization , and giving them the ability to leverage industry-first capabilities like MongoDB Queryable Encryption (which allows customers to encrypt, store, and perform queries directly on data), with MongoDB Atlas customers can spend less time managing infrastructure and more time delivering experiences. MongoDB Atlas’s integrated capabilities to support multi-modal data types and use cases—like full-text and vector search , stream processing , and data federation —accelerate innovation, helping enterprises quickly respond to market changes, power AI-driven insights, and deliver meaningful digital experiences to their end users—all without the burden of operational complexity. Modernizing and building for the future In our opinion, the Gartner Magic Quadrant provides organizations with a clear and accessible evaluation framework to identify solutions that fit their needs, today and tomorrow. The placement of MongoDB in the Leader quadrant for Cloud Database Management Systems—for the third year in a row!—validates the efforts MongoDB has made to help developers and organizations take advantage of their most valuable resource, their data. I talk to MongoDB customers frequently, and many say the same thing: in today’s digital-first economy, AI-powered applications and scalable data infrastructure aren’t just advantages, they’re absolute necessities. They say that the time to act is now, and they’re looking for solutions that will help them innovate, streamline, and seize the AI-driven future. And when it comes to modernizing their operations, they consistently point to MongoDB as their go-to partner. Begin your cloud journey with MongoDB Atlas today. Contact our sales team or register for a free account to begin building! And to learn how MongoDB can help accelerate your AI journey, visit the MongoDB AI Applications Program page. Footnotes Gartner, Magic Quadrant for Cloud Database Management Systems,  Henry Cook, Ramke Ramakrishnan, et al., 18 December 2024 GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally, and MAGIC QUADRANT is a registered trademark of Gartner, Inc. and/or its affiliates and are used herein with permission. All rights reserved. Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose. 1 Gartner Peer Insights, Cloud Database Management Systems, December 2024 https://www.gartner.com/reviews/market/cloud-database-management-systems 2 Gartner Research Methodologies, Gartner Magic Quadrant, 20 December 2024 https://www.gartner.com/en/research/methodologies/magic-quadrants-research

December 23, 2024

Using Agentic RAG to Transform Retail With MongoDB

In the competitive world of retail and ecommerce, it’s more important than ever for brands to connect with customers in meaningful, personalized ways. Shoppers today expect relevant recommendations, instant support, and unique experiences that feel tailored just for them. Enter retrieval-augmented generation (RAG) : a powerful approach that leverages generative AI and advanced search capabilities to deliver precise insights on demand. For IT decision-makers, the key challenge lies in integrating operational data with unstructured information—which can span object stores (like Amazon S3 and SharePoint), internal wikis, PDFs, Microsoft Word documents, and more. Enterprises must unlock value from curated, reliable internal data sources that often hold critical yet hard-to-access information. By combining RAG’s capabilities with these data assets, retailers can find contextually accurate information. For example, they can seamlessly surface needed information like return policies, refund processes, shipment details, and product recalls, driving operational efficiency and enhancing customer experiences. To provide the most relevant context to a large language model (LLM) , traditional RAG (which has typically relied on vector search) needs to be combined with real-time data in an operational database, the last conversation captured in a customer relationship management API call to a REST endpoint, or both. RAG has evolved to become agentic—that is, it’s capable of understanding a user inquiry and translating it to determine which path to use and which repositories to access to answer the question. MongoDB Atlas and Dataworkz provide an agentic RAG as a service solution that enables retailers to combine operational data with relevant unstructured data to create transformational experiences for their customers. MongoDB Atlas stores and unifies diverse data formats—such as customer purchases, inventory levels, and product descriptions—making them easily accessible. Dataworkz then transforms this data into vector embeddings, enabling a multistep agentic RAG pipeline to retrieve and create personalized, context-aware responses in real time. This is especially powerful in the context of customer support, product recommendations, and inventory management. When customers interact with retailers, Dataworkz dynamically retrieves real-time data from MongoDB Atlas, and, where needed, combines it with unstructured information to generate personalized AI responses, enhancing the customer experience. This architecture improves engagement, optimizes inventory, and provides scalable, adaptable AI capabilities, ultimately driving a more efficient and competitive retail operation. Reasons for using MongoDB Atlas and Dataworkz MongoDB Atlas and Dataworkz work together to deliver agentic RAG as a service for a smarter, more responsive customer experience. Here’s a quick breakdown of how: Vector embeddings and smart search: The Dataworkz RAG builder enables anyone to build sophisticated retrieval mechanisms that turn words, phrases, or even customer behaviors into vector embeddings—essentially, numbers that capture their meaning in a way that’s easy for AI to understand—and store them in MongoDB Atlas. This makes it possible to search for content based on meaning rather than exact wording, so search results are more accurate and relevant. Scalable, reliable performance: MongoDB Atlas’s cloud-based, distributed setup is built to handle high-traffic retail environments, minimizing disruptions during peak shopping times. Deep context with Dataworkz’s agentic RAG as a service: Retailers can build agentic workflows powered by RAG pipelines that combine lexical and semantic search with knowledge graphs to fetch the most relevant data from unstructured operational and analytical data sources before generating AI responses. This combination gives ecommerce brands the power to personalize experiences at a vastly larger scale. Figure 1: Reference architecture for customer support chatbots with Dataworkz and MongoDB Atlas Retail e-commerce use cases So how does this all work in practice? Here are some real-world examples of how MongoDB Atlas and Dataworkz are helping ecommerce brands create standout experiences. Building smarter customer-support chatbots Today’s shoppers want quick, accurate answers, and RAG makes this possible. When a customer asks a chatbot, “Where’s my order?” RAG enables the bot to pull the latest order and shipping details stored in MongoDB Atlas. Even if the question is phrased differently—say, “I need my order status”—the RAG-powered vector search can interpret the intent and fetch the correct response. As a result, the customer gets the help they need without waiting on hold or navigating complex menus. Personalizing product recommendations Imagine a customer who’s shown interest in eco-friendly products. With MongoDB Atlas’s vector embeddings, a RAG-powered system can identify this preference and adjust recommendations accordingly. So when the customer returns, they see suggestions that match their style—like organic cotton clothing or sustainably sourced kitchenware. This kind of recommendation feels relevant and thoughtful, making the shopping experience more enjoyable and increasing the chances of a purchase. Creating dynamic marketing content Marketing thrives on fresh, relevant content. With MongoDB Atlas managing product data and Dataworkz generating personalized messages, brands can send out dynamic promotions that truly resonate. For example, a customer who browsed outdoor gear might receive a curated email with top-rated hiking boots or seasonal discounts on camping equipment. This kind of targeted messaging feels personal, not pushy, building stronger customer loyalty. Enhancing site search experiences Traditional e-commerce searches often rely on exact keyword matches, which can lead to frustrating dead ends. But with MongoDB Atlas Vector Search and Dataworkz’s agentic RAG, search can be much smarter. For example, if a customer searches for “lightweight travel shoes,” the system understands that they’re looking for comfortable, portable footwear for travel, even if none of the product listings contain those exact words. This makes shopping smoother and more intuitive and less of a guessing game. Understanding trends in customer sentiment For e-commerce brands, understanding how customers feel can drive meaningful improvements. With RAG, brands can analyze reviews, social media comments, and support interactions to capture sentiment trends in MongoDB Atlas. Imagine a brand noticing a spike in mentions of “too small” in product reviews for a new shoe release—this insight lets them quickly adjust sizing info on the product page or update their stock. It’s a proactive approach that shows customers they’re being heard. Interactions that meet customers where they are In essence, MongoDB Atlas and Dataworkz’s RAG models enable retailers to make e-commerce personalization and responsiveness smarter, more efficient, and easier to scale. Together, they help retailers deliver exactly what customers are looking for—whether it’s a personalized recommendation, a quick answer from a chatbot, or just a better search experience. In the end, it’s about meeting customers where they are, with the information and recommendations they need. With MongoDB and Dataworkz, e-commerce brands can create that kind of connection—making shopping easier, more enjoyable, and ultimately more memorable. Learn more about Dataworkz on MongoDB by visiting dataworkz.com . The Dataworkz free tier is powered by MongoDB Atlas Vector Search .

December 23, 2024

MongoDB’s 2024 Year in Review

It’s hard to believe that another year is almost over! 2024 was a transformative year for MongoDB, and it was marked by both innovation and releases that further our commitment to empowering customers, developers, and partners worldwide. So without further ado, let’s dive into MongoDB’s 2024 highlights. We’ll also share our executive team’s predictions of what 2025 might have in store. A look back at 2024 MongoDB 8.0: The most performant version of MongoDB ever In October we released MongoDB 8.0 , the fastest, most resilient, secure, and reliable version of MongoDB yet. Architectural optimizations in MongoDB 8.0 have significantly improved the database’s performance, with 36% faster reads and 59% higher throughput for updates. Our new architecture also makes horizontal scaling cheaper and faster. Finally, working with encrypted data is easier than ever, thanks to the addition of range queries in Queryable Encryption (which allows customers to encrypt, store, and perform queries directly on data). Whether you’re a startup building your first app, or you’re a global enterprise managing mission-critical workloads, MongoDB 8.0 offers unmatched power and flexibility, solidifying MongoDB’s place as the world’s most popular document database. Learn more about what makes 8.0 the best version of MongoDB ever on the MongoDB 8.0 page . Delivering customer value with the MongoDB AI Applications Program AI applications have become a cornerstone of modern software, and MongoDB is committed to equipping customers with the technology, tools, and support they need to succeed on their AI journey. That’s why we launched the MongoDB AI Applications Program (MAAP) in 2024, a comprehensive program designed to accelerate the development of AI applications. By offering customers resources like access to AI specialists, an ecosystem of leading AI and tech companies, and AI architectural best practices supported by integrated services, MAAP helps solve customers’ most pressing business challenges, unlocks competitive advantages, and accelerates time to value for AI investments. Overall, MAAP’s aim is to set customers on the path to AI success. Visit the MongoDB AI Applications Program page or watch our session from AWS re:Invent to learn more! Advancing AI with MongoDB Atlas Vector Search In 2024, MongoDB further cemented its role in the AI space with enhancements to MongoDB Atlas Vector Search . Recognized in 2024 (for the second consecutive year!) as one of the most loved vector databases , MongoDB continues to provide a scalable, unified, and secure platform for building cutting-edge AI use cases. Recent advancements like vector quantization in Atlas Vector Search help deliver even more value to our customers, enabling them to scale applications to billions of vectors at a lower cost. Head over to our Atlas Vector Search quick start guide to get started with Atlas Vector Search today, or visit our AI resources hub to learn more about how MongoDB can power AI applications. Search Nodes: Performance at scale Search functionality is indispensable in modern applications, and with Atlas Search Nodes, organizations can now optimize their search workloads like never before. By providing dedicated infrastructure for Atlas Search and Vector Search workloads, Search Nodes ensure high performance (e.g., a 40–60% decrease in query times), scalability, and reliability, even for the most demanding use cases. As of this year , Search Nodes are generally available across AWS, Google Cloud, and Microsoft Azure. This milestone underscores MongoDB’s commitment to delivering powerful solutions that scale alongside our customers’ needs. To learn more about Search Nodes, check out our documentation or watch our tutorial . Looking ahead: MongoDB’s 2025 predictions After the excitement of the past few years, 2025 will be defined by ensuring that technology investments deliver tangible value. Organizations remain excited about the potential AI and emerging technologies hold to solve real business challenges, but are increasingly focused on maintaining a return on investment. “Enterprises need to innovate faster than ever, but speed is no longer the only measure of success. Increasingly, organizations are laser-focused on ensuring that their technology investments directly address critical business challenges and provide clear ROI and competitive advantage—whether it’s optimizing supply chains, delivering hyper-personalized customer experiences, or scaling operations efficiently,” said Sahir Azam, Chief Product Officer at MongoDB. “In 2025, I expect to see organizations make significant strides in driving this innovation and efficiency by applying AI to more production use cases and by maturing the way they leverage their data to build compelling and differentiated customer experiences.” Indeed, we expect to see organizations make more strategic investments in emerging technologies like gen AI—innovating with a sharp focus on solving business challenges. “In 2025, we can expect the focus to shift from ‘what AI can do’ to ‘what AI should do,’ moving beyond the hype to a clearer understanding of where AI can provide real value and where human judgment is still irreplaceable,” said Tara Hernandez, VP of Developer Productivity at MongoDB. “As we advance, I think we’ll see organizations begin to adopt more selective, careful applications of AI, particularly in areas where stakes are high, such as healthcare, finance, and public safety. A refined approach to AI development will be essential—not only for producing quality results but also to build trust, ensuring these tools genuinely support human goals rather than undermining them.” With more capable, accessible application development tools and customer-focused programs like MAAP at developers’ fingertips, 2025 is an opportunity to make a data-driven impact faster than ever before. "Right now, organizations have an opportunity to leverage their data to reimagine how they do business, to more effectively adapt to a changing world, and to revolutionize our quality of life,” said Andrew Davidson, SVP of Products at MongoDB. “By harnessing our latest technologies, developers can build a foundation for a transformative future." Head over to our updates page to learn more about the new releases and updates from MongoDB in 2024. Keep an eye on our events page to learn what's to come from MongoDB in 2025!

December 19, 2024

MongoDB Atlas Integration with Ably Unlocks Real-time Capabilities

Enterprises across sectors increasingly realize that data, like time, doesn’t wait. Indeed, harnessing and synchronizing information in real time is the new currency of business agility. Enter the alliance between MongoDB and Ably—a partnership that has led to Ably's new database connector for MongoDB Atlas . The new database connector provides a robust framework for businesses to create real-time, data-intensive applications that can provide top-notch user experiences thanks to an opinionated client SDK to be used on top of LiveSync, ensuring both data integrity and real-time consistency—without compromising your existing tech stack. The synergy of MongoDB Atlas and Ably LiveSync This new MongoDB Atlas-Ably integration tackles a fundamental challenge in modern application architecture: maintaining data consistency across distributed systems in real-time. MongoDB Atlas serves as the foundation—a flexible, scalable database service that adapts to the ebb and flow of data demands. Meanwhile, Ably LiveSync acts as the nervous system, ensuring that every change, every update, resonates instantly across the entire application ecosystem. The Ably LiveSync database connector for MongoDB Atlas offers a transformative approach to real-time data management, combining unparalleled scalability with seamless synchronization. This solution effortlessly adapts to growing data volumes and expanding user bases, catering to businesses of all sizes—from agile startups to established enterprises. By rapidly conveying database changes to end-users, it ensures that all stakeholders operate from a single, up-to-date source of truth, fostering data consistency across the entire organization. At its core, LiveSync is built with robust resilience in mind, featuring built-in failover mechanisms and connection recovery capabilities. This architecture provides businesses with the high availability they need to maintain continuous operations in today's always-on digital landscape. Moreover, by abstracting away the complexities of real-time infrastructure, LiveSync empowers developers to focus on creating features that drive business value. This focus on developer productivity, combined with its scalability and reliability, positions Ably LiveSync for MongoDB Atlas as a cornerstone technology for companies aiming to harness the power of real-time data synchronization. Figure 1: Ably real-time integration with MongoDB Atlas. Industry transformation: A real-time revolution This new integration has a number of implications across various sectors. For example, in the banking and financial services sector , the MongoDB Atlas-Ably integration enables instantaneous fraud detection systems that can promptly react to potential threats. Live trading platforms benefit as well, seamlessly updating to reflect every market change as it happens. Banking applications are equally enhanced, with real-time updating of account balances and transactions, ensuring that users always have access to the most recent financial information. In the retail industry , meanwhile, the integration facilitates real-time inventory management across both physical and online stores, ensuring that supply matches demand at all times. This capability supports dynamic pricing strategies that can adapt instantly to fluctuations in consumer interest, and it powers personalized shopping experiences with live product recommendations tailored to individual customer preferences. Manufacturing and mobility sectors also see transformative benefits. With the capability for real-time monitoring of production lines, businesses can implement just-in-time manufacturing processes, streamlining operations and reducing waste. Real-time tracking of vehicles and assets enhances logistics efficiency, while predictive maintenance systems provide foresight into potential equipment failures, allowing for timely interventions. The healthcare sector stands to gain significantly from this technology. Real-time patient monitoring systems offer healthcare providers immediate alerts, ensuring swift medical responses when necessary. Electronic health records receive seamless updates across multiple care settings, promoting coherent patient care. Efficient resource allocation is achieved through live tracking of hospital beds and equipment, optimizing hospital operations. Insurance companies are not left out of this technological leap. The integration allows for dynamic risk assessment and pricing models that adapt in real-time, refining accuracy and responsiveness. Instant claim processing and status updates enhance customer satisfaction, while live tracking of insured assets facilitates more accurate underwriting and expedites the resolution of claims. Finally, in telecommunications and media this integration promises buffer-free content delivery and streaming services, vastly improving the end-user experience. real-time network performance monitoring enables proactive issue resolution, maintaining service quality. Users can enjoy synchronized experiences across multiple devices and platforms, fostering seamless interaction with digital content. Today's business imperative As industries continue to evolve at a rapid pace, the integration of MongoDB Atlas and Ably LiveSync provides a compelling way for businesses to not only keep up but lead the real-time revolution. For IT decision-makers looking to put their organizations at the forefront of innovation, this integration turns static data into a dynamic driver of business growth and market leadership. Access MongoDB Atlas and Ably LiveSync Resources and start your journey towards real-time innovation today. Learn more about how MongoDB Atlas can power industry-specific solutions .

December 18, 2024

Leveraging BigQuery JSON for Optimized MongoDB Dataflow Pipelines

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

December 17, 2024

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

Checkpointers and Native Parent Child Retrievers with LangChain and MongoDB

MongoDB and LangChain, the company known for its eponymous large language model (LLM) application framework, are excited to announce new developments in an already strong partnership. Two additional enhancements have just been added to the LangChain codebase, making it easier than ever to build cutting-edge AI solutions with MongoDB. Checkpointer support In LangGraph, LangChain’s library for building stateful, multi-actor applications with LLMs, memory is provided through checkpointers . Checkpointers are snapshots of the graph state at a given point in time. They provide a persistence layer, allowing developers to interact and manage the graph’s state. This has a number of advantages for developers—human-in-the-loop, "memory" between interactions, and more. Figure adapted from “Launching Long-Term Memory Support in LangGraph”. LangChain Blog. Oct. 8, 2024. https://blog.langchain.dev/launching-long-term-memory-support-in-langgraph/ MongoDB has developed a custom checkpointer implementation, the " MongoDBSaver " class, that, with just a MongoDB URI (local or Atlas ), can easily store LangGraph state in MongoDB. By making checkpointers a first-class feature, developers can have confidence that their stateful AI applications built on MongoDB will be performant. That’s not all, since there are actually two new checkpointers as part of this implementation— one synchronous and one asynchronous . This versatility allows the new functionality to be even more versatile, and serving developers with a myriad of use cases. Both implementations include helpful utility functions to make using them painless, letting developers easily store instances of StateGraph inside of MongoDB. A performant persistence layer that stores data in an intuitive way will mean a better end-user experience and a more robust system, no matter what a developer is building with LangGraph. Native parent child retrievers Second, MongoDB has implemented a native parent child retriever inside LangChain. This approach enhances the performance of retrieval methods utilizing the retrieval-augmented Generation (RAG) technique by providing the LLM with a broader context to consider. In essence, we divide the original documents into relatively small chunks, embed each one, and store them in MongoDB. Using such small chunks (a sentence or a couple of sentences) helps the embedding models to better reflect their meaning. Now developers can use " MongoDBAtlasParentDocumentRetriever " to persist one collection for both vector and document storage. In this implementation, we can store both parent and child documents in a single collection while only having to compute and index embedding vectors for the chunks. This has a number of performance advantages because storing vectors with their associated documents means no need to join tables or worry about painful schema migrations. Additionally, as part of this work, MongoDB has also added a " MongoDBDocStore " class which provides many helpful utility functions. It is now easier than ever to use documents as a key-value store and insert, update, and delete them with ease. Taken together, these two new classes allow developers to take full advantage of MongoDB’s abilities. MongoDB and LangChain continue to be a strong pair for building agentic AI—combining performance and ease of development to provide a developer-friendly experience. Stay tuned as we build out additional functionality! To learn more about these LangChain integrations, here are some resources to get you started: Check out our tutorial . Experiment with checkpointers and native parent child retrievers to see their utility for yourself. Read the previous announcement with LangChain about AI Agents, Hybrid Search, and Indexing.

December 16, 2024