Applications
Customer stories, use cases, and experiences of MongoDB
Leveraging an Operational Data Layer for Telco Success
The emergence of 5G network communication, IoT devices, edge computing, and AI have accelerated structural changes within the telecommunications industry, creating new needs and opportunities. To remain competitive, telcos must embrace this technology-driven transformation by defining a robust data strategy. Such a strategy should enhance operational efficiency and provide unique value to customers, and should ultimately enable telcos to set themselves apart from their competitors. All of this can be attained by leveraging an operational data layer (ODL) with MongoDB. Operating a consolidated ODL opens new business opportunities that telcos can incorporate into their value matrix, including customer support systems, AI-enriched applications, and IoT-oriented services. These unlocked capacities will help telecommunications companies succeed in a competitive market. Understanding the operational data layer An ODL is an architectural pattern that centrally integrates and organizes siloed enterprise data, making it available to consuming applications. It acts as an intermediary between data producers and consumers. This architecture pattern is illustrated below: Figure 1. ODL sample reference architecture, using MongoDB In this diagram, MongoDB Atlas acts as the ODL, centrally integrating siloed data from multiple sources, including CRM, HR, and billing. Initially, data is extracted to the ODL, transformed according to established requirements, and then loaded to the MongoDB database. By means of delta loads, the ODL is kept in sync over time. Consuming applications, both operational and analytical, access the ODL through an API layer, which delivers a common set of methods for users, and enforces security standards throughout the organization. Enhancing operational efficiency with MongoDB and the ODL At its core, implementing an ODL with MongoDB provides access to a rich document model and a data developer platform that boosts operational efficiency and unlocks the value of previously siloed enterprise data. The ODL attains this efficiency through a set of key capabilities inherent to MongoDB. The ODL benefits from the flexibility of the document model that adapts its schema to any application requirement while supporting multiple data structures. This polymorphic structure allows variations from document to document liberating applications from rigid schemas and supporting merging from non-identical entities. Telcos gain speed in development—which translates to better performance—when accessing data through an ODL, as they avoid costly join operations required by legacy applications. MongoDB provides a unique place for data storage that can be accessed in a single database operation decreasing end-user response times. Telcos can leverage MongoDB’s versatility to cast multiple workloads, store any data type, and to adopt a rich query language that executes complex operations. Subsequently, the ODL accepts sophisticated query pipelines capable of processing text, images, videos, geospatial data, facet search, analytical transformations, time series, and more. Horizontal and vertical scalability empowers telcos to receive large data volumes and high traffic loads essential for modern applications. This mechanism is achieved through sharding, a process that partitions and distributes data across multiple nodes, accommodating fluctuating workload demands and enhancing overall system performance. An ODL running in MongoDB Atlas benefits from a multi-cloud strategy that allows deployments across multiple cloud providers. This approach mitigates vendor lock-in risks, grants global coverage, and adapts to infrastructure requirements—ensuring that applications adhere to cost constraints, achieve performance benchmarks, and maintain regulatory compliance. MongoDB provides a robust security framework for storing and managing sensitive data due to its built-in tools—including encryption, authentication, authorization, network security, and auditing—thus protecting data against information breaches. It also complies with important international regulations for telcos like the General Data Protection Regulation (GDPR) and the Payment Card Industry Data Security Standard (PCI DSS). MongoDB provides a modern data platform designed to build, manage and scale applications in a unified developer experience. The developer platform fosters innovation allowing developers to access a variety of features to manage their ODL including Atlas Vector Search, Atlas Monitoring, and Atlas Triggers, among others. Refer to our official documentation to learn more about MongoDB Atlas . Using the ODL to gain a competitive advantage Fostering operational efficiency through an ODL is the initial step toward opening a new business that will eventually translate into a competitive advantage. Accordingly, telcos need to develop their own strategies and capitalize on the benefits from these unlocked opportunities, differentiating themselves in the industry. Well-known telcos have already leveraged this approach, creating successful business outcomes. They consolidate single-view instances , concentrating information from different business lines—such as mobile, fixed lines, broadband, and TV/entertainment—into MongoDB Atlas. This environment is well-suited for building personalized customer management solutions, overcoming challenges with siloed data environments. These telcos choose MongoDB because it offers a flexible data model that facilitates data aggregation and horizontal scaling, allowing them to efficiently leverage customer data to build customer-centric applications. Additionally, leading telcos are leveraging AI to enhance their operations, safeguard their business, and improve their services. One prominent use of AI is fraud detection and prevention . This is a critical area that, if poorly managed, can lead to negative consequences like financial losses, unmeasurable reputational damage, and unhinged security network risks. A consolidated ODL serves as a gateway for implementing fraud detection measures. Nowadays, MongoDB’s platform is ingesting and storing terabytes of data from multiple platforms to leverage AI models, potentially saving millions of dollars for telcos. Refer to our ebook, Innovate with AI: The Future Enterprise , to learn more. Telcos are also capitalizing on their networks and the MongoDB ODL by effectively managing the vast amounts of data generated by IoT devices, and adding new end-to-end services. MongoDB is helping large telcos effectively implement IoT platforms supplying scalability for growing device demand, flexibility to manage data model changes, and automatic data tiering to reduce storage costs. These capabilities ultimately improve customer experiences and speed time to market for new applications. Furthermore, ODLs improve product catalog management systems, which are increasingly common in the industry due to telcos’ expanding their offering to a broader set of products, from phone plans to bundled entertainment services. ODLs upgrade the product catalog, allowing for real-time product personalization and analytics. MongoDB assists telcos in upgrading their product catalog systems, enabling advanced search capabilities, reducing development time, and supporting seasonal workload demand. Refer to our white paper, Implementing an Operational Data Layer for Product Catalog Modernization , to learn more. Finally, an ODL accelerates the modernization of monolithic relational database systems that struggle to manage exponential data growth and to adapt to evolving business needs. Telcos use MongoDB in their modernization efforts to deliver 3 to 5x faster operations, allowing scaling to millions of records per day, while at the same time reducing their costs—typically by 50% or more. Future directions This blog highlights how implementing an ODL with MongoDB can unlock telcos’ ability to achieve operational efficiency through the native capabilities of MongoDB and its cloud offering. This innovative architecture not only improves operations, but also unlocks business opportunities that are the foundation for new competitive advantages. These enhanced capabilities represent the backbone to consolidate telcos’ strategic positioning, ultimately differentiating from their competitors in powerful ways. Visit our MongoDB for Telecommunications solutions page to learn more. If you would like to learn more about implementing an ODL with MongoDB for your TELCO organization, visit the following resources: White paper: Implementing an Operational Data Layer White paper: Unleash Telco Transformation with an Operational Data Layer Head over to our quick-start guide to get started with Legacy Modernization today.
Retail Insights With MongoDB: Shoptalk Fall
The retail industry has continued to evolve into an omnichannel marketplace since the 2020 pandemic sparked a surge of online shipping. Now, nearly five years later, the line between in-person shopping and e-commerce has grown thinner, thanks to technological advancements and shifting consumer expectations. The advent of AI and a focus on generative AI (gen AI) has made these shifts especially prominent. Shoptalk Fall 2024 focused on how to apply these technologies to consumer behavior, merchandising, supply chain optimization, and the like. As a retail principal in MongoDB’s industry solutions team, I manned our booth and walked the exhibit floor, answering this question: How can MongoDB Atlas —a flexible, cloud-enabled developer data platform—solve many data challenges that retail enterprises face? Let’s explore some of the key themes that emerged at Shoptalk Fall 2024, including unified commerce, AI-driven innovation, and operational efficiency. 1. Unified commerce: Seamless integration across channels Unified commerce is often touted as a transformative concept, yet it represents a long-standing challenge for retailers—disparate data sources and siloed systems. It’s less of a revolutionary concept and more of a necessary shift to make long-standing problems more manageable. In a sense, it’s “old wine in a new bottle,” unifying fragmented data ecosystems to serve an omnichannel experience. In essence, unified commerce is the integration of physical and digital retail channels, and it is essential for delivering a frictionless customer experience. However, the complexity of managing data silos and diverse technology sprawling across diverse platforms is a major challenge for a wide variety of enterprises. We’re working with retailers globally to simplify cross-channel data unification into an operational data layer that enables easy synchronization across e-commerce, social and mobile platforms, and physical stores. This data platform approach with built-in text search and vector search , enables retailers to facilitate a superior customer experience with powerful search and gen AI capabilities on their e-commerce or mobile portals. A great example is CarGurus , which manages vast amounts of real-time data across its platform and supports seamless, personalized user experiences online and in person. Figure 1. Reference architecture of an operational data layer built on MongoDB Atlas, capable of serving multiple types of customer requests across engagement channels. 2. AI and data-driven innovation: Personalization at scale Across several major retailers, changes indicate that AI is reshaping retail, enabling hyperpersonalized experiences and data-driven decisions. However, the success of AI models hinges on the quality, accessibility, and scalability of data. Without a flexible, powerful data platform, scaling AI initiatives across a retailer’s data landscape becomes daunting. AI adoption requires vast amounts of structured and unstructured data. The reliance on aging infrastructure and legacy data estates significantly hinders retailers’ ability to adopt transformative innovations like gen AI, as doing so demands substantial upgrades to their underlying data architecture. Fragmented technology ecosystems—with disparate AI and machine learning (ML) systems and siloed data estates lacking integrated frameworks—further complicate this modernization journey. Retailers that we work with use MongoDB’s efficient handling of unstructured data combined with vector search to build AI-enabled applications. The aggregation framework enables powerful real-time data processing, and we have a broad ecosystem of integrations with AI platforms to trigger algorithms in real-time. These can fuel data-driven personalization engines to deliver tailored product recommendations and targeted marketing campaigns. Figure 2. Operational data, analytical insights, and unstructured data combine to form a data layer for AI-enabled applications. 3. Supply chain optimization: Operational efficiency Operational efficiency was a key focus at Shoptalk, particularly in improving supply chain management and inventory optimization in real-time. Retailers struggle with legacy systems that are not equipped to handle modern data processing needs. Traditional database systems often lack the real-time data processing ability necessary for today’s fast-paced environment, which can lead to costly delays. To drive operational efficiency by building real-time data processing capabilities (critical for supply chain optimization and many other use cases), a retail organization needs a single view of data entities. It also needs to be able to track inventory levels, forecast demand, and optimize logistics using live data streams from Internet of Things devices, sensors, and external partners. Delivering real-time or near real-time insights on inventory, stock locations, and other critical information empowers the workforce, enhancing team efficiency and development across the organization. To consolidate inventory data from different regions into a centralized view, MongoDB’s flexible data model can handle disparate data. At the same time, real-time triggers and change streams update applications instantly when inventory levels fluctuate. With these capabilities, MongoDB provides a robust platform for building a resilient, responsive supply chain capable of handling global expansion and complex logistics requirements, ultimately reducing stockouts, optimizing fulfillment, and improving the customer experience. For example, Lidl built an automatic stock reordering application for its branches and warehouses to increase efficiency along the supply chain when placing orders. In doing so, it addressed the challenge of complex data structures and an enormous volume of data to be processed. Figure 3. Reference architecture showing how MongoDB becomes the central part of the solution for supply chain optimization. 4. Product innovation and assortment: Agile data management At Shoptalk, speakers also highlighted product innovation as a key driver for retail success. Retailers are moving toward rapid product development cycles and diverse product assortments. Product innovation and assortment management are vital as retailers work to capture consumer interest and meet evolving demands. Retailers often need a flexible system that can support rapid product iteration and the addition of new attributes, without delays. Agile and quick product-catalogs management requires a data platform that can deploy rapid updates and support complex product catalogs with ease. MongoDB’s flexible document-oriented model enables retailers to store and manage diverse product attributes without predefined schemas or evolving schemas as needed, making it easy to integrate data from different catalog systems while retaining flexibility for rapid updates and new product attributes. This consolidated view helps streamline catalog management and enables retail teams to easily track product availability, pricing, and specifications across channels. When combining this view with sales data in MongoDB Atlas, retailers gain powerful real-time insights into consumer preferences, demand patterns, and emerging trends. With MongoDB’s aggregation framework and real-time analytics capabilities, retailers can quickly analyze sales trends against product data to identify high-performing products, seasonal trends, and gaps in the market. For instance, by evaluating purchase patterns, retailers can identify attributes or combinations (e.g., color, style, or size) that drive higher sales, informing future product development and marketing strategies. MongoDB Atlas’s data integration capabilities enable retailers to incorporate additional data sources, such as customer feedback or social media insights, to enrich product and sales data. This comprehensive, multifaceted analysis enables data-driven decisions that can refine product assortments and inform new product launches, maximizing the chance of success in the market. 5. Customer loyalty and trust Customer loyalty programs have evolved dramatically in recent years. Consumers are expecting personalized interactions and rewards without any delay in retailers understanding their behavior. However, effectively managing and utilizing customer data for loyalty initiatives requires advanced data management capabilities. Customer loyalty programs are increasingly personalized, with retailers leveraging data to build trust and deliver consistent value. Retailers need to build sophisticated loyalty programs by understanding real-time customer data. The biggest challenge that retailers encounter is consolidating all customer data, such as transactions, loyalty profiles, and shopping behavior, stored across several operational systems. As discussed earlier, MongoDB Atlas makes it easy to bring diverse datasets into a single database, enabling data access as required by any consumer of that data. Once the data is consolidated and established using real-time data feeds, retailers can use MongoDB Atlas Charts to visualize customer engagement trends and respond proactively with personalized offers and rewards. The end-to-end encryption and compliance features built into MongoDB Atlas help make sure that customer data is secure, fostering trust and supporting adherence to data privacy regulations. Learn how L’Oréal created several apps and improved customer experiences by championing personalized, inclusive, and responsible beauty at scale. 6. Growth opportunities: Agile scalability Enterprises today often aim to expand their digital reach and scale their operations globally. As retailers expand their footprints into new markets, they encounter different requirements in terms of languages, product assortments, and customer expectations. Managing data across multiple geographies and ensuring fast access is a considerable challenge that is difficult to achieve with traditional databases. As retailers reach new markets, scalability becomes a pressing concern. Figure 4. Modern retailers distribute their data globally to provide customers with low-latency access. For multinational retailers looking to expand geographically, MongoDB helps them build distributed architectures (sometimes even multi-cloud ) to deliver fast, low-latency access for customers worldwide. MongoDB Atlas offers built-in scalability features, including horizontal scaling, that provide fast performance at any scale. With its workload isolation capabilities , real-time operations can continue seamlessly because the analytics workloads can be segregated to eliminate resource contention. Learn how Commercetools modernized its composable commerce platform using MongoDB Atlas and MACH architecture and achieved amazing throughput for Black Friday 2023 . Enabling the future of retail with MongoDB Atlas As the key themes of Shoptalk Fall 2024, unified commerce, AI-driven innovation, and operational efficiency all highlight the critical need for a flexible and scalable data platform. MongoDB Atlas answers these challenges with its robust, cloud-native architecture, offering retailers the tools they need to thrive in an evolving landscape. From real-time data processing and global scalability to advanced AI integrations, MongoDB Atlas empowers retailers to stay competitive and deliver exceptional customer experiences. By adopting MongoDB Atlas, retailers can unlock the full potential of their data, streamline operations, and future-proof their businesses in an increasingly complex retail environment. Want to learn more about MongoDB in the retail industry? Read our Essential Elements to Ecommerce Modernization E-book on our retail page today.
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.
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.
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.
Commerce at Scale: Zepto Reduces Latency by 40% With MongoDB
Zepto is one of the fastest-growing Indian startups and a pioneer in introducing quick commerce to India. Quick commerce, sometimes referred to as “Q-commerce” is a new, faster form of e-commerce promising ultra-quick deliveries, typically in less than one hour. Founded in July 2021, Zepto has revolutionized the Indian grocery delivery industry, offering users a choice of over 15,000 products with a promised 10-minute delivery. Since its launch, the company has rapidly expanded its operations, recording 20% monthly growth and achieving annualized sales of $1.5 billion by July 2024. Zepto’s order processing and delivery system is instrumental in meeting its promise to customers. Zepto’s system routes new orders to a “dark store,” where bleeding-edge assignment systems help pack orders in under 75 seconds. A proprietary navigation system ensures riders can then deliver these orders promptly. As Zepto expanded, its monolithic infrastructure, based on a relational SQL database, could not achieve the scalability and operational efficiency the company needed. Zepto changed the game by turning to MongoDB Atlas . Mayank Agarwal, Senior Architect at Zepto, shared the company’s journey with MongoDB during a presentation at MongoDB.local Bengaluru in September 2024 . “We had a big monolith. All the components were being powered by PostgreSQL and a few Redis clusters,” said Agarwal. “As our business was scaling, we were facing a lot of performance issues, as well as restrictions in terms of the velocity at which we wanted to operate.” Zepto’s legacy architecture posed four key issues: Performance bottlenecks: As Zepto grew, the need for complex database queries increased. These queries required multiple joins, which put a significant strain on the system, resulting in high CPU usage and an inability to provide customers and delivery partners with accurate data. Latency: Zepto needed its API response times to be fast. However, as the system grew, background processing tasks slowed down. This led to delays and caused the system to serve stale data to customers. A need for real-time analytics: Teams on the ground, such as packers and riders, required real-time insights on stock availability and performance metrics. Building an extract, transform, and load (ETL) pipeline for this was both time-consuming and resource-intensive. Increased data scaling requirements: Zepto’s data was growing exponentially. Managing it efficiently became increasingly difficult, especially when real-time archival and retrieval were required. MongoDB Atlas meets Zepto’s goals “We wanted to break our monolith into microservices and move to a NoSQL database . But we wanted to evaluate multiple databases,” said Agarwal. Zepto was looking for a document database that would let its team query data even when the documents were structured in a nested fashion. The team also needed queryability on array-based attributes or columns. MongoDB fulfilled both use cases. “Very optimally, we were able to do some [proofs of concept]. The queries were very performant, given the required indexes we had created, and that gave us confidence,” said Agarwal. “The biggest motivation factor was when we saw that MongoDB provides in-memory caching , which could address our huge Redis cluster that we couldn’t scale further.” Beyond scalability, MongoDB Atlas also provided high reliability and several built-in capabilities. That helped Zepto manage its infrastructure day to day, and create greater efficiencies for both its end users and its technical team. Speaking alongside Agarwal at MongoDB.local Bengaluru, Kshitij Singh, Technical Lead for Zepto, explained: “When we discovered MongoDB Atlas, we saw that there were a lot of built-in features like the MongoDB chat support , which gave us very qualitative insights whenever we faced any issues. That was an awesome experience for us.” Data archival , sharding support , and real-time analytic capabilities were also key in helping the Zepto team improve operational efficiencies. With MongoDB, Zepto was able to deploy new features more quickly. Data storage at the document level meant less management overhead and faster time to market for new capabilities. Furthermore, MongoDB’s archival feature made it easier for Zepto to manage large datasets. The feature also simplified the setup of secondary databases for ETL pipelines, reducing the heavy lifting for developers. “You go on the MongoDB Atlas platform and can configure archival in just one click,” said Singh. Zepto reduces latency, handles six times more traffic, and more The results of migrating to MongoDB Atlas were immediate and significant: Zepto saw a 40% reduction in latency for some of its most critical APIs, which directly improved the customer experience. Postmigration, Zepto’s infrastructure could handle six times more traffic than before, without any degradation in performance. This scalability enabled the company to continue its rapid growth without bottlenecks. Page load times improved by 14% , leading to higher conversion rates and increased sales. MongoDB’s support for analytical nodes helped Zepto segregate customer-facing workloads from internal queries. This ensured that customer performance was never compromised by internal reporting or analytics. “MongoDB is helping us grow our business exponentially,” said Agarwal at the end of his presentation. Visit our product page to learn more about MongoDB Atlas.
Goodnotes Finds Marketplace Success Using MongoDB Atlas
In the fast-paced world of app development, creating a feature-rich digital marketplace that scales effectively can be challenging. Goodnotes was founded in 2010 with the aim of replacing traditional paper notebooks with a digital alternative that reimagines the note-taking experience. Since then, the app has gone through several iterations and grown in popularity, now with more than 24 million monthly active users and 2.5 billion notes. The team behind Goodnotes spoke at MongoDB.local Hong Kong in September 2024. They shared their journey of using MongoDB Atlas and MongoDB Atlas Search to build and run a comprehensive marketplace that expands the company’s offerings, catering to its growing number of content creators. “At the beginning of 2023, we launched a pop-up shop, which was a very simple version of the marketplace, to test the water, and we realized it got really popular,” said Xing Dai, Principal Backend Engineer at Goodnotes. The full Goodnotes Marketplace launched in August 2024 as a space where content creators can enhance their note-taking experience by purchasing additional content, such as planners, stickers, and textbooks. Building a robust digital marketplace with MongoDB Atlas “The first and the most difficult challenge [was] that we are a multiplatform app, and if you want to launch on multiple platforms, you need to support different app stores as well as [the] web,” said Dai. Using MongoDB Atlas, Dai’s team created a fully configurable marketplace that would be accessible on different mobile and desktop platforms and the web. The initial pop-up shop’s infrastructure consisted of a Payload content management system connected to a MongoDB database. However, building a full-fledged marketplace was more challenging. The architecture needed to be scalable and include search, ordering, and customization capabilities. “With [MongoDB] Atlas, it was really easy to add the in-app purchase and build the subscription infrastructure to manage the purchase workflow,” said Dai. The Goodnotes team introduced NestJS—a JavaScript API framework—to build client APIs. It then developed a user-friendly portal for the operations team and for creators who want to upload new products. Finally, the team built a full event-based data pipeline on top of MongoDB. “What’s nice is that everything on the marketplace is actually configurable in the backend,” said Dai. “We don’t need to do anything other than configuring what we need to store in the database, and the iOS client will connect it to the backend.” “When we want to extend the marketplace to other platforms, nothing needs to be changed,” Dai added. “We only need to configure different shops for different platforms.” This means that Goodnotes can easily make its marketplace available on different app platforms, such as Apple and Android, and on the web. Adding searches, charts, and soon AI As Goodnotes added more products to its marketplace, users had difficulty finding what they wanted. Despite having limited resources, the Goodnotes team endeavored to build a comprehensive search function. Using MongoDB Atlas Search and MongoDB Atlas Triggers , the team built a search function that would generate the search view collection by-products and attributes, combining them into one collection. The team then added an Atlas Search index for the search field with an API exposing the search. “We also added an auto-complete function, which is very similar to search, in the sense that we just had to create a function to generate aggregated collections, trigger it using [MongoDB] Atlas Triggers, and add the index and expose it in the marketplace,” said Dai. The search function is now popular among marketplace users, making it quick and easy for them to find what they are looking for. Goodnotes also regularly uses MongoDB Atlas Charts . For example, it creates charts showing how many products there are in the system over time. One of the key next steps for Goodnotes involves using generative AI to translate product descriptions and content into different languages (the app is currently available in 11 languages). The team also wants to introduce personalized recommendations for a more tailored experience for each user. Ending the MongoDB.local presentation, Dai said: “MongoDB made it very fast and easy to build the whole marketplace and our search feature on top of the database using [MongoDB] Atlas Search. The solution scales, and so far we haven’t had any performance issues.” Visit our product page to learn more about MongoDB Atlas .
Customer Service Expert Wati.io Scales Up on MongoDB
Wati.io is a software-as-a-service (SaaS) platform that empowers businesses to develop conversation-driven strategies to boost growth. Founded by CEO Ken Yeung in 2019, Wati started as a chatbot solution for large enterprises, such as banks and insurance companies. However, over time, Yeung and his team noticed a growing need among small and medium-sized businesses (SMBs) to manage customer conversations more effectively. To address this need, Wati used MongoDB Atlas and built a solution based on the WhatsApp Business API. It enables businesses to manage and personalize conversations with customers, automate responses, improve commerce functions, and enhance customer engagement. Speaking at MongoDB.local Hong Kong in September 2024, Yeung said, “The current solutions on the market today are not good enough. Especially for SMBs [that] don’t have the same level of resources as enterprises to deal with the number of conversations and messages that need to be handled every day.” Supporting scale: From MongoDB Community Edition to MongoDB Atlas “From the beginning, we relied on MongoDB to handle high volumes of messaging data and enable businesses to manage and scale their customer interactions efficiently,” said Yeung. Wati originally used MongoDB Community Edition , as the company saw the benefits of a NoSQL model from the beginning. As the company grew, it realized it needed a scalable infrastructure, so Wati transitioned to MongoDB Atlas. “When we started reaching the 2 billion record threshold, we started having some issues. Our system slowed down, and we were not able to scale it,” said Yeung. Atlas has now become an essential part of Wati’s infrastructure, helping the company store and process millions of messages each month for over 10,000 customers in 165 countries. “Transitioning to a new platform—MongoDB Atlas—seamlessly was critical because our messaging system needs to be on 24/7,” said Yeung. Wati collaborated closely with the MongoDB Professional Services and MongoDB Support teams, and in a few months it was able to rearchitect the deployment and data model for future growth and demand. The work included optimizing Wati’s database by breaking it down into clusters. Wati then focused on extracting connections, such as conversations, and dividing and categorizing data within the clusters—for example, qualifying data as cold or hot based on the read and write frequencies. This architecture underpins the platform’s core features, including automated customer engagement, lead qualification, and sales management. Deepening search capabilities with MongoDB Atlas Search For Wati’s customers, the ability to search through conversation histories and company documents to retrieve valuable information is a key function. This often requires searching through millions of records to rapidly find answers so that they can respond to customers in real-time. By using MongoDB Atlas Search , Wati improved its search capabilities, ultimately helping its business customers perform more advanced analytics and improve their customer service agents’ efficiency and customer reporting. “[MongoDB] Atlas Search is really helpful because we don’t have to do a lot of technical integration, and minimal programming is required,” said Yeung. Looking ahead: Using AI and integrating more channels Wati expects to continue collaborating with MongoDB to add more features to its platform and keep innovating at speed. The company is currently exploring to build more AI capabilities of Wati KnowBot , as well as how it can expand its integration with other conversation platforms and channels such as Instagram and Facebook. To learn more about MongoDB Atlas, visit our product page . To get started with MongoDB Atlas Search, visit the Atlas Search product page .
Hanabi Technologies Uses MongoDB to Power AI Assistant, Hana
For all the hype surrounding generative AI, cynics tend to view the few real-world implementations as little more than “fancy chatbots.” But for Abhinav Aggarwal, CEO of Hanabi Technologies , the idea of a generative AI-powered bot that is more than just an assistant was intriguing. “I’d been using ChatGPT since it launched,” said Aggarwal. “That got me thinking: How could we make a chatbot that was like a team member?” And with that concept, Hana was born. The problem with bots “Most generative AI chatbots do not act like people; they wait for a command and give a response,” said Aggarwal. “We wanted to create a human-like chatbot that would proactively help people based on what they wanted—automating reminders, for example, or fetching time zones from your calendar to correctly schedule meetings.” Hanabi’s flagship product, Hana, is an AI assistant designed to enhance team collaboration within Google Chat, working in concert with Google Workspace and its suite of products. “Our target customers are smaller companies of between 10 and 50 people. At this size you’re not going to build your own agent from scratch,” he said. Hana integrates with Google APIs to deliver a human-like assistant that chimes in with helpful interventions, such as automatically setting reminders and making sure meetings are booked in the right time zone for each participant. “Hana is designed to bring AI to smaller companies and help them collaborate in a space where they are already working—Google Workspace,” Aggarwal explained. The MongoDB Atlas solution For Hana to act like a member of the team, Hanabi needed to process massive amounts of data to support advanced features like retrieval-augmented generation (RAG) for better information retrieval across Google Docs and many other sources. And with a rapidly growing user base of over 600 organizations and 17,000+ installs, Hanabi also required a secure, scalable, and high-performing data storage solution. MongoDB Atlas provided a flexible document model, built-in vector database, and scalable cloud-based infrastructure, freeing Hanabi engineers to build new features for Hana rather than focusing on rote tasks like data extract, transform, and load processes or manual scaling and provisioning. Now, MongoDB Atlas handles a variety of responsibilities: Scalability and security: MongoDB Atlas’s auto-scaling and automatic backup features have enabled Hanabi to seamlessly grow its user base without the need for manual database management. RAG: MongoDB Atlas plays a critical role in Hana’s RAG functionality. The platform enables Hanabi to split Google Docs into small sections, create embeddings, and store these sections in Atlas’s vector database. Development Processes: According to Aggarwal, MongoDB’s flexibility in managing changing schemas has been essential to the company’s fast-paced development cycle. Data Visualization: Using MongoDB Atlas Charts has enabled Hanabi to create comprehensive dashboards for real-time data visualization. This has helped the team track usage, set reminders, and optimize performance without needing to build a manual dashboard. Impact and results With MongoDB Atlas, Hanabi can successfully scale Hana to meet the demands of its rapidly expanding user base. The integration is also enabling Hana to offer powerful features like automatic interactions with customers, advanced information retrieval from Google Docs, and manually added memory snippets, making it an essential tool for teams around the world. Next steps Hanabi plans to continue integrating more tools into Hana while expanding its reach to personal Gmail users. The company is also rolling out a new automatic-interaction feature, further enhancing Hana’s ability to proactively assist users without direct commands. MongoDB Atlas remains a key component of Hanabi’s stack, alongside Google Kubernetes Engine, NestJS, and LangChain, enabling Hanabi to focus on innovating to improve the customer experience. Tech Stack MongoDB Atlas Google Kubernetes Engine NestJS LangChain Are you building AI apps? Join the MongoDB AI Innovators Program today! Successful participants gain access to free MongoDB Atlas credits, technical enablement, and invaluable connections within the broader AI ecosystem. If your company is interested in being featured, we’d love to hear from you. Connect with us at ai_adopters@mongodb.com.
3 Ways MongoDB EA Azure Arc Certification Serves Customers
One reason more than 50,000 customers across industries choose MongoDB is the freedom to run anywhere—across major cloud providers, on-premises in data centers, and in hybrid deployments. This is why MongoDB is always working to meet customers where they are. For example, many customers choose MongoDB Atlas (which is available in more than 115 cloud regions across major cloud providers) for a fully managed experience. Other customers choose MongoDB Enterprise Advanced (EA) to self-manage their database deployments to meet specific on-premises or hybrid requirements. To that end, we’re pleased to announce that MongoDB EA is one of the first certified Microsoft Azure Arc-enabled Kubernetes applications, which provides customers even more choice of where and how they run MongoDB. Customer adoption of Azure Arc has grown by leaps and bounds. This new certification, and the launch of MongoDB EA as an Arc-enabled Kubernetes application on Azure Marketplace , means that more customers will be able to leverage the unparalleled security, availability, durability, and performance of MongoDB across environments with the centralized management of their Kubernetes deployments. We are very excited to have MongoDB available for our customers on the Azure Marketplace. By extending Azure Arc’s management capabilities to your MongoDB deployments, customers gain the benefit of centralized governance, enhanced security, and deeper insights into database performance. Azure Arc makes hybrid database management with MongoDB efficient and consistent. Collaboration between MongoDB and Microsoft represents an opportunity for many of our customers to further accelerate their digital transformation when building enterprise-class solutions with Azure Arc. Christa St Pierre, Partner Group Manager, Azure Edge Devices, Microsoft Here are three ways the launch of MongoDB EA on Azure Marketplace for Arc-enabled Kubernetes applications gives customers greater flexibility. 1. MongoDB EA supports multi-Kubernetes cluster deployments, simplifies management MongoDB Enterprise Advanced seamlessly integrates market-leading MongoDB capabilities along with robust enterprise support and tools for self-managed deployments at any scale. This powerful solution includes advanced automation, comprehensive auditing, strong authentication, reliable backup, and insightful monitoring capabilities, all of which work together to ensure security compliance and operational efficiency for organizations of any size. The relationship between MongoDB and Kubernetes is one of strong synergy. With Kubernetes, MongoDB EA really can run anywhere, such as a single deployment spanning on-premises and more than one public cloud Kubernetes cluster. Customers can use the MongoDB Enterprise Kubernetes Operator, a key component of MongoDB Enterprise Advanced, to simplify the management and automation of self-managed MongoDB deployments in Kubernetes. This includes tasks like creating and updating deployments, managing backups, and integrating with various Kubernetes services. The ability of the MongoDB Enterprise Kubernetes Operator to deploy and manage MongoDB deployments that span multiple Kubernetes clusters significantly enhances resilience, improves disaster recovery, and minimizes latency by allowing data to be co-located closer to where it is needed, ensuring optimal performance and reliability. 2. Azure Arc complements MongoDB EA, providing centralized management While MongoDB Enterprise Advanced is already among a select group of databases capable of operating across multiple Kubernetes clusters , it is now also supported in Azure Arc-enabled Kubernetes environments. Azure Arc enables the standardized management of Kubernetes clusters across various environments—including in Azure, on-premises, and even other clouds—while harnessing the power of Azure services. Azure Arc accomplishes this by extending the Azure control plane to standardize security and governance across a wide range of resources and locations. For instance, organizations can centrally monitor all of the Azure Arc-enabled Kubernetes clusters using Azure Monitor for containers , or they can enforce threat protection at scale using Microsoft Defender for Kubernetes. This centralized control significantly reduces the complexity of managing Kubernetes clusters running anywhere, as customers can oversee all resources and apply consistent security and compliance policies across their hybrid environment. 3. Customers can leverage the resilience of MongoDB EA and the centralized governance of Azure Arc Together, these solutions empower organizations to build robust applications across a wide array of environments, whether on-premises or in multi-cloud settings. The combination of MongoDB Enterprise Advanced and the MongoDB Enterprise Operator simplifies the deployment of MongoDB across Kubernetes clusters, allowing organizations to fully leverage enhanced resilience and geographic distribution that surpasses the capabilities of a single Kubernetes cluster. Azure Arc further enhances this synergy by providing centralized management for all of these Kubernetes clusters, regardless of where they are running; for customers running entirely in the public cloud, we recommend using MongoDB’s fully managed developer data platform, MongoDB Atlas. If you’re interested in learning more, we invite you to explore the Azure Marketplace listing for MongoDB Enterprise Advanced for Arc-enabled Kubernetes applications. Please note that aside from use for evaluation and development purposes, this offering requires the purchase of a MongoDB Enterprise Advanced subscription. For licensing inquiries, we encourage you to reach out to MongoDB at https://www.mongodb.com/contact to secure your license and to begin harnessing the full potential of these powerful solutions.
Accelerating MongoDB Migration to Azure with Microsoft Migration Factory
Migrating MongoDB workloads from on-premises solutions or other cloud platforms to MongoDB Atlas on Azure has never been simpler, thanks to Microsoft’s Cloud Migration Factory (CMF). This newly created program is perfect for organizations using MongoDB Enterprise Advanced or Community Edition who are ready to modernize. By transitioning to MongoDB Atlas —an integrated suite of data and applications services—customers can simplify their database management, enhance performance, and reduce operational complexities, unlocking new potential and value from their data. Why the Microsoft Cloud Migration Factory (CMF)? The Microsoft CMF offers hands-on delivery for eligible workloads to accelerate customer journeys on Azure at no cost. With repeatable best practices, robust tools, structured processes, and a skilled resource pool, the Microsoft CMF delivery model mitigates technical risk and accelerates deployments with optimized architectures to maximize platform benefits. The MongoDB Migration Factory, meanwhile, is a comprehensive program designed to help organizations migrate their existing databases to MongoDB. This program provides a structured approach, tools, and best practices to ensure a smooth and efficient migration process. Microsoft CMF is partnering with MongoDB Migration Factory to jointly deliver migrations of MongoDB Enterprise Advanced or Community Edition deployments to MongoDB Atlas on Azure in a secure, optimized, and customer-focused way. This comprehensive migration approach enables businesses to leverage Azure for their MongoDB-based solutions with speed, confidence, best practices, and minimal disruption risk at an optimized cost. “This joint delivery offering from Microsoft Cloud Migration Factory (CMF) and MongoDB Migration Factory is designed to accelerate AI transformation priorities for our customers by driving the migrations to MongoDB Atlas on Azure with speed and quality,” said Rashida Hodge, Corporate Vice President of Azure Data and AI at Microsoft. “We have delivered thousands of customer engagements with the CMF model across all Azure workloads, making it a proven approach for accelerating cloud journeys with Microsoft-owned delivery.” Why MongoDB Atlas on Azure? MongoDB Atlas on Azure combines MongoDB’s robust document data platform with Azure’s scalability and advanced cloud services, making it ideal for high-performance applications. Offering features like automatic scaling, high availability, and comprehensive security, MongoDB Atlas on Azure supports diverse workloads, including transaction processing, in-app analytics, and full-text search. Integrations with Azure services—including Azure Synapse Analytics, Microsoft Fabric, and Power BI—enhance MongoDB Atlas’s analytics and visualization capabilities, and compliance with standards like HIPAA and GDPR ensures data privacy, enabling organizations to focus on innovation in a secure, scalable environment. Figure 1: MongoDB Atlas on Azure Integrations ecosystem Migrating MongoDB Community Edition or Enterprise Advanced to MongoDB Atlas on Azure Migrating from MongoDB Community Edition or MongoDB Enterprise Advanced to MongoDB Atlas on Azure offers numerous benefits, including enhanced scalability, security, and operational efficiency. MongoDB Atlas is a fully managed, cloud-based solution that simplifies database management by handling tasks like automatic scaling, high availability, and data backup. Leveraging Azure’s infrastructure, Atlas provides integrated services such as Azure Active Directory for improved authentication and identity management, and global cloud coverage to reduce latency by deploying clusters closer to users. MongoDB Atlas on Azure also includes robust security features like encryption at rest and in transit, network isolation, and advanced access controls, meeting compliance standards. These features are often difficult to implement in a self-managed environment. Additionally, Atlas offers advanced monitoring and automated tuning tools for optimizing database performance and resource usage, helping to reduce costs over time. For organizations considering migration to MongoDB Atlas, Microsoft CMF offers end-to-end guidance, providing a clear roadmap for every stage of the migration process, from initial validation to post-migration testing. With flexible migration paths that cater to a range of needs, Microsoft CMF supports live migrations using tools like mongosync and offline migrations with MongoDB’s native tools, enabling everything from minimal-downtime transitions to complete re-hosting. Best of all, Microsoft CMF is a complimentary service, which means that organizations don’t need to worry about budgets and can focus on the transition to MongoDB Atlas on Azure. In collaboration with MongoDB Professional Services, the CSX team leveraged MongoDB and Microsoft Migration Factory to migrate a mission-critical railroad transportation app quickly and seamlessly with zero downtime. John Maio, Department Head, Enterprise Data & Analytics at CSX Getting started Microsoft CMF’s structured approach guides organizations through each critical milestone to ensure a smooth migration process. For those interested in migrating their MongoDB setup to Azure, contact MongoDB today to take advantage of this free migration opportunity and experience the ease of MongoDB Atlas on Azure with Microsoft CMF support.
MongoDB Database Observability: Integrating with Monitoring Tools
This post is the final in a three-part series on leveraging database observability. Welcome back to our series on Leveraging Database Observability! Our previous post showcased a real-world use case highlighting how MongoDB Atlas’s observability tools effectively tackle database performance challenges. Whether you’re a developer, DBA, or DevOps engineer, our mission is to empower you to harness the full potential of your data through our observability suite . Integrating Atlas metrics with your central enterprise observability tools can simplify your operations. By seamlessly working with popular observability tools, our approach helps teams streamline workflows and enhance visibility across systems. Integrating MongoDB Atlas with third-party monitoring tools MongoDB’s developer data platform combines all essential data services for building modern applications within a unified experience. Our purpose-built observability tools for Atlas environments offer automatic monitoring and optimization, guiding diagnostics tailored specifically for MongoDB. Additionally, we extend Atlas metrics into your existing enterprise observability stack, enabling seamless integration without replacing your current tools. This creates a consolidated, single-pane view that unifies Atlas telemetry with other tech and application metrics, ensuring comprehensive visibility into both database and full-stack performance. This integration empowers you to monitor, receive alerts, and make data-driven decisions within your existing workflows, driving greater efficiency. Below is a quick guide to modifying integration settings through the Atlas UI and the popular integrations we support: Navigate to the Project Integrations page in Atlas. Choose the organization and project you want to configure from the navigation bar. On the Project Integrations page, select the third-party services you’d like to integrate. Configure the chosen services with the required API keys and regions. Critical integrations for your observability platform With Atlas’s Datadog and Prometheus integrations, you can send critical MongoDB metrics to these platforms, empowering detailed, real-time monitoring. Through Datadog , you can track database operation counts, query efficiency, and resource usage, ideal for pinpointing bottlenecks and managing resources. Similarly, Prometheus enables you to monitor essential metrics like query times, connection rates, and memory usage, supporting flexible tracking of database health and performance. Both integrations facilitate proactive detection of issues, alert configuration for resource thresholds, and a cohesive view of Atlas data when visualized in Grafana. Atlas’s integration with PagerDuty streamlines incident management by sending metrics like performance alerts, billing anomalies, and security events directly to PagerDuty. This integration records incidents automatically, notifies teams upon alerts, and supports two-way syncing, ensuring resolved alerts in Atlas are reflected in PagerDuty. It enables efficient incident response and resource allocation to maintain system stability. With Atlas integrations for Microsoft Teams and Slack, you can route key metrics—such as query latency, disk usage, and throughput—to these channels for timely updates. Teams can use these insights for real-time performance monitoring, incident response, and collaboration. Notifications through these platforms ensure your team stays informed on database performance, storage health, and user activity changes as they occur. Use case: Centralized observability with MongoDB Atlas, Datadog, and Slack Let’s walk through a hypothetical scenario for ShopSmart, an e-commerce company that leverages MongoDB Atlas to manage its product catalog and customer data. As traffic surges, the DevOps team faces challenges in monitoring application performance and database health effectively. To tackle these challenges, the team leverages MongoDB Atlas’ integration with Datadog and Slack, creating a powerful observability ecosystem. Integrating MongoDB Atlas with Datadog: The team pushes key MongoDB Atlas metrics into Datadog, such as query performance, connection counts, and Atlas Vector Search metrics. With Datadog, they can visualize these metrics and correlate overall MongoDB performance with their other applications. Out-of-the-box monitors and dedicated dashboards allow the team to track metrics like throughput, average read/write latency, and current connections. This visibility helps pinpoint bottlenecks in real time, ensuring optimal database performance and improving overall application responsiveness. Setting up alerts in Datadog: The team configures alerts for critical metrics like high query latency and increased error rates. When thresholds are breached, Datadog instantly notifies the team. This proactive approach allows the team to address potential performance issues before they impact customers. Integrating Datadog with Slack: To ensure fast communication, alerts are sent directly to the dedicated Slack channel, “ShopSmart-Alerts.” This integration fosters seamless collaboration, enabling the team to discuss and resolve issues in real-time. With these integrations, ShopSmart’s engineering team can monitor performance quickly and address issues efficiently. The unified observability approach enhances operational efficiency, improves the customer experience, and supports ShopSmart’s competitive edge in the e-commerce industry. By leveraging MongoDB Atlas, Datadog, and Slack, the team ensures scalable performance and drives continuous innovation. Conclusion MongoDB Atlas empowers developers and organizations to achieve unparalleled observability and control over their database environments. By seamlessly integrating with central enterprise observability tools, Atlas enhances your ability to monitor performance metrics and ensures you can do so within your existing workflows. This means you can focus on building modern applications confidently, knowing you have the insights and alerts necessary to maintain optimal performance. Embrace the power of MongoDB Atlas and transform your approach to database management—because your applications can thrive when your data is observable. And that wraps up our Leveraging Database Observability series! We hope you learned something new and found value in these discussions. Sign up for MongoDB Atlas , our cloud database service, to see database observability in action. To dive deeper and expand your knowledge, check out this learning byte for more insights on the MongoDB observability suite and how it can enhance your database performance.