Applications
Customer stories, use cases, and experiences of MongoDB
Busting the Top Myths About MongoDB vs Relational Databases
February 10, 2025
Applied
Advancing Encryption in MongoDB Atlas
Maintaining a strong security posture and ensuring compliance with regulations and industry standards are core responsibilities of enterprise security teams. However, satisfying these responsibilities is becoming increasingly complex, time-consuming, and high-stakes. The rapid evolution of the threat landscape is a key driver of this challenge. In 2024, the percentage of organizations that experienced a data breach costing $1 million or more jumped from 27% to 36%. 1 This was partly fueled by a 180% surge from 2023 to 2024 in vulnerability exploitation by attackers. 2 Concurrently, regulations are tightening. Laws like the Health Insurance Portability and Accountability Act (HIPAA) 3 and the U.S. Securities and Exchange Commission’s cybersecurity regulations 4 have introduced stricter security requirements. This has raised the bar for compliance. Thousands of enterprises rely on MongoDB Atlas to protect their sensitive data and support compliance efforts. Encryption plays a crucial role on three levels; securing data at rest, in transit, and in use. However, security teams need more than solely strong encryption. Flexibility and control are essential to align with an organization’s specific requirements. MongoDB is introducing significant upgrades to MongoDB Atlas encryption to meet these needs. This includes enhanced customer-managed key (CMK) functionality and support for TLS 1.3. This post explores these improvements, along with the planned deprecation of outdated TLS versions, to strengthen organizations’ security postures. Why customer-managed keys (CMKs) matter Customer-managed keys (CMKs) are a security and data governance feature that delivers enterprises full control over the encryption keys protecting their data. With CMKs, customers can define and manage their encryption strategy. This ensures they have ultimate authority over access to their sensitive information. MongoDB Atlas customer key management provides file-level encryption, similar to transparent data encryption (TDE) in other databases. This customer-managed encryption-at-rest feature works alongside always-on volume-level encryption 5 in MongoDB Atlas. CMKs ensure all database files and backups are encrypted. MongoDB Atlas also integrates with AWS Key Management Service (AWS KMS), Azure Key Vault , and Google Cloud KMS . This ensures customers have the flexibility to manage keys as part of their broader enterprise security strategy. Customers using CMKs retain complete control of their encryption keys. If an organization needs to revoke access to data due to a security concern or any other reason, it can do so immediately by freezing or destroying the encryption keys. This capability acts as a “kill switch,” ensuring sensitive information becomes inaccessible when protection is critical. Similarly, an organization can destroy the keys to render the data and backups permanently unreadable and irretrievable. This may be applicable should they choose to retire a cluster permanently. Announcing CMK over private networking As part of a commitment to deliver secure and flexible solutions for enterprise customers, MongoDB is introducing CMKs over private networking. This enhancement enables organizations to manage their encryption keys without exposing their key management service (KMS) to the public internet. Using CMKs in MongoDB Atlas previously required Azure Key Vault and AWS KMS to be accessible via public IP addresses prior to today. While functional, this posed challenges for customers who need to keep KMS traffic private. It forced those customers to either expose their KMS endpoints or manage IP allow lists. By using private networking, customers can now: Eliminate the need for public IP exposure. Simplify network management by removing the need to manage allowed IP addresses. This reduces administrative effort and misconfiguration risk. Align with organizational requirements that mandate the use of private networking. Customer key management over private networking is now available for Azure Key Vault and AWS KMS . Customers can enable and manage this feature for all their MongoDB Atlas projects through the MongoDB Atlas UI or the MongoDB Atlas Administration API . More enhancements are coming for MongoDB customer key management in 2025. These include secretless authentication mechanisms and CMKs for search nodes. MongoDB Atlas TLS enhancements advance encryption in transit Securing data in transit is equally vital as a foundation of encryption at rest with CMKs. To address this, MongoDB Atlas enforces TLS by default. This ensures encrypted communication across all aspects of the platform, including client connections. Now MongoDB is reinforcing its TLS implementation with key enhancements for enterprise-grade security. MongoDB is in the process of rolling out fleetwide support for TLS 1.3 in MongoDB Atlas. The latest version of the cryptographic protocol offers several advantages over its predecessors. This includes stronger security defaults, faster handshakes, and reduced latency. Concurrently, TLS versions 1.0 and 1.1 are being deprecated. The rationale for this is known weaknesses and their inability to meet modern security standards. MongoDB is aligning with industry best practices by standardizing on TLS 1.2 and 1.3. This ensures a secure communication environment for all MongoDB Atlas users. Additionally, MongoDB now offers custom cipher suite selection, giving enterprises more control over their cryptographic configurations. This feature lets organizations choose the cipher suites for their TLS connections, ensuring compliance with their security requirements. Achieving encryption everywhere This post covers how MongoDB secures data at rest with CMKs and in transit with TLS. However, what about data in use while it’s being processed in a MongoDB Atlas instance? That’s where Queryable Encryption comes in. This groundbreaking feature enables customers to run expressive queries on encrypted data without ever exposing the plaintext or keys outside the client application. Sensitive data and queries never leave the client unencrypted. This ensures sensitive information is protected and inaccessible to anyone without the keys, including database administrators and MongoDB itself. MongoDB is committed to providing enterprise-grade security that evolves with the changing threat and regulatory landscapes. Organizations now have greater control, flexibility, and protection across every stage of the data lifecycle with enhanced CMK functionality, TLS 1.3 adoption, and custom cipher suite selection. As security challenges grow more complex, MongoDB continues to innovate to enable enterprises to safeguard their most sensitive data. To learn more about these encryption enhancements and how they can strengthen your security posture, visit MongoDB Data Encryption . 1 PwC , October 2024 2 Verizon Data Breach Investigations Report , 2024 3 U.S. Department of Health and Human Services , December 2024 4 U.S. Securities and Exchange Commission , 2023 5 MongoDB Atlas Security White Paper , Encryption at Rest section page 12
Hasura: Powerful Access Control on MongoDB Data
Across industries—and especially in highly regulated sectors like healthcare, financial services, and government—MongoDB has been a preferred modern database solution for organizations handling large volumes of sensitive data that require strict compliance adherence. In such enterprises, secure access to data via APIs is critical, particularly when information is distributed across multiple MongoDB databases and external data stores. Hasura extends and enhances MongoDB's access control capabilities by providing granular permissions at the column and field level across multiple databases through its unified interface. At the same time, designing a secure API system from scratch to meet this need takes significant development resources and becomes a burden to maintain and update. Hasura solves this problem for enterprises by elegantly serving as a federated data layer, with robust access control policies built-in. Hasura enforces powerful access control rules across data domains, joins data from multiple sources, and exposes it to the user via a single API. In this blog, we'll explore how Hasura and MongoDB work together to empower teams with granular data access control while simplifying data retrieval across collections. Team-specific data domains First, Hasura makes it possible for a business unit or team to own a set of databases and collections, also known as a data domain. Within each domain, a team can connect any number of MongoDB databases and other data sources, allowing the domain to have fine-grained role-based access control (RBAC) and attribute-based access control (ABAC) across all sources. More important though, is the ability to enable relationships that span domains, effectively connecting data from various teams or business units and exposing it to a verified user as necessary. This granular permissioning system means that the right users can access the right data at the right time, without compromising security. Field-level access control Hasura’s MongoDB connector also provides a powerful, declarative way to define access control rules at the collection and field level. For each MongoDB collection, roles may be specified for read, create, update, and delete (CRUD) permissions. Within those permissions, access may be further restricted based on the values of specific attributes. By defining these rules declaratively, Hasura makes it easy to implement and reason about complex access control policies. Joining across collections In addition to enabling granular access control, Hasura simplifies the retrieval of related data across multiple databases. By inspecting your MongoDB collections, Hasura can automatically create schemas and API endpoints (in GraphQL, REST, etc.) that let you query data along with its relationships. This eliminates the need to manually stitch together data from different collections in your application code. Instead, a graph of related data can be easily retrieved in a single API call, while still having that data filtered through your access control rules. As companies wrestle with the challenges of secure data access across sprawling database environments, Hasura provides a compelling solution. By serving as a federated data layer on MongoDB and external data, Hasura enables granular access control through a combination of role-based permissions, attribute-based restrictions, and the ability to join data and apply access across sources. Figure 1. Hasura & MongoDB demo environment With Hasura’s MongoDB connector , teams can easily implement sophisticated data access policies in a declarative way and provide their applications with secure access to the data they need. This combination of security and simplicity makes Hasura and MongoDB a powerful solution for organizations that strive to modernize, especially those in industries with strict compliance requirements. Visit the MongoDB Resources Hub to learn more about MongoDB Atlas.
Debunking MongoDB Myths: Enterprise Use Cases
MongoDB is frequently viewed as a go-to database for proof-of-concept (POC) applications. The flexibility of MongoDB’s document model enables teams to rapidly prototype and iterate. This allows for adaptation of the data model as requirements evolve during the early stages of application development. It is common for applications to continuously evolve during initial development. However, moving an application to production requires developers to add validation logic and fully define the data structures. A frequent assumption is that because MongoDB data models can be flexible, they can not be structured. However, while MongoDB does not require a defined schema, it does support them. MongoDB allows users to precisely calibrate rules and enforcement levels for every component of data. This enables a level of granular control that traditional databases, with their all-or-nothing approach to schema enforcement, struggle to match. Data model flexibility is not a binary choice between "schemaless" or "strictly enforced." More accurately, it exists on a spectrum in MongoDB. Users can incrementally define schemas in parallel with the overall “hardening” of the application. MongoDB's approach to data modeling makes it an ideal platform for business-critical applications. It is designed to support the entire application lifecycle; from nascent concepts and initial prototypes, to global rollouts of production environments. Enterprise-grade features like ACID transactions and industry-leading scalability ensure MongoDB can meet the demands of any modern application. Learning from the past So why do misconceptions persist regarding MongoDB? These perceptions originated over a decade ago. Teams working with MongoDB back in 2014 or earlier faced challenges when deploying it in production. Applications could slow down under heavy loads, data consistency was not guaranteed when writing to multiple documents, and teams lacked tools to monitor and manage deployments effectively. As a result, MongoDB gained a perception of being unsuitable for specific use cases or critical workloads. This perception has persisted despite a decade of subsequent development and innovation . Therefore, this is now an inaccurate assessment of today’s preeminent document database. MongoDB has evolved into a mature platform that directly addresses these historical pain points. Today’s MongoDB delivers robust tooling, guaranteed consistency, and comprehensive data validation capabilities. Myth: MongoDB is a niche database What are the top use cases for MongoDB? This question is difficult to answer because MongoDB is a general-purpose database that can support any use case. The document model is the primary driver of MongoDB’s versatility. Documents are similar to JSON objects with data being represented as key-value pairs. Values can be simple types like strings or numbers. However, values can also be arrays or nested objects which allows documents to easily represent complex hierarchical structures. The document model's flexibility allows data to be stored exactly as the application consumes it. This enables highly efficient writing and optimizes data for retrieval without needing to set up standard or materialized views, although both are supported . While MongoDB is no longer a niche database, it does have advanced capabilities to support niche requirements. The aggregation pipeline provides a powerful framework for data analytics and transformation. Time-series collections store and query temporal data efficiently to support IoT and financial applications. Geospatial indexes and queries enable location-based applications to perform complex proximity calculations. MongoDB Atlas includes native support for vector search . This enabled Cisco to experiment with generative AI use cases and streamline their applications to production. MongoDB handles the diverse data requirements that power modern applications. The document model provides the foundation for general use. Concurrently, advanced features ensure teams do not need to integrate additional tools as application requirements evolve. The result is a single platform that can grow from prototype to production, handling general requirements and specialized workloads with equal proficiency. Myth: MongoDB is not suitable for enterprise-grade workloads A common perception is that MongoDB works well for small applications but falls short at enterprise scale. Ironically, many organizations first consider MongoDB while struggling to scale their relational databases. These organizations have discovered MongoDB’s architecture is specifically designed to support scale-out distributed deployments. While MongoDB matches relational databases in vertical scaling capabilities, the document model enables a more natural and intuitive approach for horizontal scaling. Related data is stored together in a single document. Therefore, MongoDB can easily distribute complete data units across shards. This contrasts with relational databases. Relational data is split across multiple tables. This makes it difficult to place all related data on the same shard. Horizontal scaling with MongoDB sets an organization up for better performance. Most MongoDB queries need to access only a single shard. Equivalent queries in a relational database often require costly cross-server communication. Telefonica Tech has leveraged horizontal scaling to nearly double their capacity with a 40% hardware reduction . MongoDB Atlas further automates and simplifies these scaling capabilities through a fully managed service built to meet demanding enterprise requirements. Atlas provides a 99.995% uptime guarantee and availability across AWS, Google Cloud, and Azure in over 100 regions worldwide. This frees teams to focus on rapid development and innovation rather than infrastructure maintenance by offloading the operational complexity of deploying and running databases at scale. Powering the enterprise applications of today and tomorrow Over 50,000 customers and 70% of the Fortune 100 rely on MongoDB to power their enterprise applications. Independent industry reports from Gartner and Forrester continue to recognize MongoDB as a leader in the database space. Do not let outdated myths prevent your organization from the competitive advantages of MongoDB's enterprise capabilities. To learn more about MongoDB, head over to MongoDB University and take our free Intro to MongoDB course . Read more about customers building on MongoDB. Read our first blog in this series about myths around MongoDB vs relational databases. Check out the full video to learn about the other 6 myths that we're debunking in this series.
MongoDB & DKatalis’s Bank Jago, Empowering Over 500 Engineers
DKatalis , a technology company specialized in developing scalable digital solutions, is the engineering arm behind Bank Jago , Indonesia’s first digital bank. An app-only institution, Bank Jago enables end-to-end banking with features such as auto budgeting. This allows Bank Jago’s customers to easily and effectively organize their finances by creating " Pockets "—for expenses like food, savings, or entertainment. Launched in 2019, Bank Jago has seen tremendous growth in only a few years, with its customer base reaching 14.1 million as of October 2024. While speaking at MongoDB.local Jakarta , Chris Samuel, Staff Engineer at DKatalis, shared how MongoDB became the data backbone of Bank Jago, and how MongoDB Atlas supported Bank Jago’s growth. Bank Jago’s journey with MongoDB started in 2019, when DKatalis built the first version of Bank Jago using the on-premise version of MongoDB: MongoDB Community Edition . “We did everything ourselves, up to the point when we realized that the bigger our user [base] grew, the more painful it was for us to monitor everything,” said Samuel. In 2021, DKatalis decided to migrate Bank Jago [from MongoDB Community Edition] to MongoDB Atlas. This first involved migrating all data to Atlas. Then the database platform had to be set up to facilitate scalability and enable improved maintenance operations in the long-term. “In terms of process, it is actually seamless,” said Samuel during his MongoDB.local talk. Specifically, MongoDB Atlas offers six key capabilities that have facilitated the bank’s daily operations, supported its fast growth, and improved efficiencies: Flexibility: MongoDB's document model supports diverse data types and adapts to Jago's dynamic requirements. Scalability: MongoDB Atlas effortlessly supports the rapid growth in user base and data volume. High performance: The platform enables fast query execution and efficient data retrieval for a seamless customer experience. Real-time capabilities: MongoDB Atlas prevents delays during transactions, account creation, and balance checking. Regulation compliance: With MongoDB Atlas, local hosting is possible. This enables DKatalis to meet Indonesian financial regulatory standards. Community support: MongoDB’s strong developer community and rich ecosystem in Jakarta fosters collaboration and learning. All of these have also helped improve efficiencies for DKatalis’s team of over 500 engineers, who are now able to reduce data architecture complexity, and focus on innovation. Fostering a great engineering culture and community with MongoDB In another talk at MongoDB.local Singapore , DKatalis’s Chief Engineering Officer, Alex Titlyanov, explained that using MongoDB has been and continues to be a great learning, upskilling, and operational experience for his team. “DKatalis has a pretty unique organizational culture when it comes to its engineering teams: there are no designated engineering managers or project managers; instead, teams are self-managed,” said Titlyanov. “This encourages a community-driven environment, where engineers are continuously upgrading their skills, particularly with tools like MongoDB.” The company has established internal communities, such as the MongoDB community led by Principal Software Engineer Boon Hian Tek. These communities focus on knowledge sharing, skill-building, and ensuring that the company’s 500 engineers are proficient in using MongoDB. This deep knowledge of MongoDB—and the ease of use offered by the Atlas platform—means that DKatalis’s engineers are also able to build their own bespoke tools to improve daily operations and meet specific needs. For example, the team has built a range of tools aimed at helping deal with the complexity and scale of Bank Jago’s data architecture. “Most traditional banks offer their customers access to six months, sometimes a year’s worth of transaction history. But Bank Jago gives access to the entire transaction history,” said Boon. The engineering team ended up having to deal with 56 different databases and 485 data collections. Some would reach 1.13 billion documents, while others receive up to 42.5 million new documents every day. Some of the bespoke tools built on MongoDB Atlas include: Index sync report: DKatalis implemented a custom-built tool using MongoDB’s Atlas API to manage database indexing automatically. This was essential given the bank’s real-time requirements. Adding indexes manually during peak hours would have disrupted performance. Daily reporting: The team built a tool to monitor for slow queries. This provides daily reports on query performance so issues can be identified and resolved quickly. Add index: The Rolling Index feature from Atlas was initially used. However, the team required greater context for each index. Therefore, they built a tool that at 3:00 am automatically checks if there are any indexes to create. The tool calls in the Atlas API to create and publish the results. Exporting metrics: The Atlas console was used to source diagrams that were helpful. However, the team required each metric to be available per database and per collection versus cluster. The team built a thin layer on top of the Atlas console to slice up the required metrics using the Atlas API. “The scalability and flexibility of MongoDB have been essential in helping the team handle the bank’s fast growth and complex feature set. MongoDB’s document-oriented structure enables us to develop innovative features like ‘Pockets’, and we continue to see MongoDB as an integral part of our technology stack in the future,” said Titlyanov. Visit our product page to learn more about MongoDB Atlas . To learn how MongoDB powers solutions in the financial services industry, visit our solutions page .
BAIC Group Powers the Internet of Vehicles With MongoDB
The Internet of Vehicles (IoV) is revolutionizing the automotive industry by connecting vehicles to the Internet. Vehicle sensors generate a wealth of data, affording manufacturers, vehicle owners, and traffic departments deep insights. This unlocks new business opportunities and enhances service experiences for both enterprises and consumers. BAIC Research Institute , a subsidiary of Beijing Automotive Group Co. (BAIC Group), is a backbone enterprise of the Chinese auto industry. Headquartered in Beijing, BAIC Group is involved in everything from R&D and manufacturing of vehicles and parts to the automobile service trade, comprehensive traveling services, financing, and investments. BAIC Group is a Fortune Global 500 company with more than 67 billion USD of annual revenue. The Institute is also heavily invested in the IoV industry. It plays a pivotal role in the research and development of the two major independent passenger vehicle products in China: Arcfox and Beijing Automotive . It is also actively involved in building vehicle electronic architecture, intelligent vehicle controls, smart cockpit systems, and smart driving technologies. To harness cutting-edge, data-driven technologies such as cloud computing, the Internet of Things, and big data, the Institute has built a comprehensive IoV cloud platform based on ApsaraDB for MongoDB . The platform collects, processes, and analyzes data generated by over a million vehicles, providing intelligent and personalized services to vehicle owners, automotive companies, and traffic management departments. At MongoDB.local Beijing in September 2024, BAIC Group’s Deputy Chief Engineer Chungang Zuo said that the BAIC IoV cloud platform facilitates data access for over a million vehicles. It also supports online services for hundreds of thousands of vehicles. Data technology acts as a key factor for IoV development With a rapid increase of vehicle ownership in recent years, the volume of data on BAIC Group’s IoV cloud platform quickly surged. This led to several data management challenges, namely the need to handle the following: Large data volumes High update frequencies Complex data formats High data concurrency Low query efficiency Data security issues The IoV platform also needed to support automotive manufacturers who must centrally store and manage a large amount of diverse transactional data. Finally, the platform is needed to enable manufacturers to leverage AI and analytical capabilities to interpret and create value from this data. BAIC Group’s IoV cloud platform reached a breaking point because the legacy databases it employed were incapable of handling the deluge of exponential vehicle data nor supporting planned AI-driven capabilities. The Institute identified MongoDB as the solution to support its underlying data infrastructure. By using MongoDB, BAIC would gain a robust core to enhance data management efficiency from the business layer to the application layer. The power of MongoDB as a developer data platform offered a wide range of capabilities. This was a game-changer for the Institute. MongoDB’s document model makes managing complex data simple Unlike traditional relational database models, MongoDB’s JSON data structure and flexible schema model are well suited for the variety and scale of the ever-changing data produced by connected vehicles. In traditional databases, vehicle information is spread across multiple tables, each with nearly a hundred fields, leading to redundancy, inflexibility, and complexity. With MongoDB, all vehicle information can be stored in a single collection, simplifying data management. Migrating vehicle information to MongoDB has significantly improved the Institute’s data application efficiency. MongoDB’s GeoJSON supports location data management The ability to accurately calculate vehicle location within the IoV cloud platform is a key benefit offered by MongoDB. Particularly, MongoDB’s GeoJSON (geospatial indexing) supports important features, such as the ability to screen vehicle parking situations. Zuo explained that during the data cleaning phase, the Institute formats raw vehicle data for MongoDB storage and outputs it as standardized cleaned data. In the data calculation phase, GeoJSON filters vehicles in a specific range. This is followed by algorithmic clustering analysis of locations to gain vehicle parking information. Finally, the Institute retrieves real-time data from the MongoDB platform to classify and display vehicle parking situations on a map for easy viewing. MongoDB provides scalability and high-performance MongoDB’s sharded cluster enhances data capacity and processing performance, enabling the Institute to effectively manage exponential IoV data growth. The querying and result-returning processes are executed concurrently in a multi-threaded manner. This facilitates continuous horizontal expansion without any downtime as data needs grow. Zuo said that a significant advantage for developers is the high self-healing capability of the sharded cluster; if a primary node fails, MongoDB automatically switches to a backup. This ensures seamless service and process integrity. Security features meet data regulatory requirements MongoDB’s built-in security features enable the IoV platform to meet rigorous data protection standards, helping the Institute stay compliant with regulatory requirements and industry standards. With MongoDB, the Institute can ensure end-to-end data encryption throughout the entire data lifecycle, including during transmission, storage, and processing, with support for executing queries directly on encrypted data. For example, during storage, MongoDB encrypts sensitive data, such as vehicle identification numbers and phone numbers. Sharding and replication mechanisms establish a robust data security firewall. Furthermore, MongoDB’s permission control mechanism enables secure database management with decentralized authority. Zuo said that MongoDB’s sharded storage and clustered deployment features ensure the platform’s reliability exceeds the 99.99% service-level agreement. MongoDB’s high concurrency capabilities enable the Institute to share real-time vehicle status updates with vehicle owners’ apps, enhancing user experience and satisfaction. In addition, MongoDB’s unique compression technology and flexible cloud server configurations reduce data storage space and resource waste. This significantly lowers data storage and application costs. BAIC uses MongoDB to prepare for future opportunities Looking ahead, Zuo Chungang stated that the BAIC IoV cloud platform has expanding demands for data development and application in three areas: vehicle data centers, application scenario implementation, and AI applications. MongoDB’s capabilities will remain core to helping address the Institute’s upcoming needs and challenges.
MongoDB Empowers ISVs to Drive SaaS Innovation in India
Independent Software Vendors (ISVs) play a pivotal role in the Indian economy. Indeed, the Indian software market is expected to experience an annual growth rate of 10.40%, resulting in a market volume of $15.89bn by 2029. 1 By developing specialized software solutions and digital products that can be bought 'off the shelf', ISVs empower Indian organizations to innovate, improve efficiency, and remain competitive. Many established enterprises in India choose a 'buy' rather than 'build' strategy when it comes to creating modern software applications. This is particularly true when it comes to cutting-edge AI use cases. MongoDB works closely with Indian ISVs across industries, providing them with a multi-cloud data platform and highly flexible, scalable technologies to build operational and efficient software solutions. For example, Intellect AI , a business unit of Intellect Design Arena, has used MongoDB Atlas to drive a number of innovative use cases in the banking, financial services, and insurance industries. Intellect AI chose MongoDB for its flexibility coupled with its ability to meet complex enterprise requirements such as scale, resilience, and security compliance. And Ambee, a climate tech startup, is using MongoDB Atlas ’ flexible document model to support its AI and ML models. Here are three more examples of ISV customers who are enabling, powering, and growing their SaaS solutions with MongoDB Atlas. MongoDB enhancing Contentstack's content delivery capabilities Contentstack is a leading provider of composable digital experience solutions, and specializes in headless content management systems (CMS). Headless CMS is a backend-only web content management system that acts primarily as a content repository. “Our headless CMS allows our customers to bring all forms of content to the table, and we host the content for them,” said Suryanarayanan Ramamurthy, Head of Data Science at Contentstack, while speaking at MongoDB.local 2024 . A great challenge in the CMS industry is the ability to provide customers with content that remains factually correct, brand-aligned, and tailored to the customer’s identity. Contentstack created an innovative, AI-based product— Brand Kit —that does exactly that, built on MongoDB Atlas. “Our product Brand Kit, which launched in June 2024, overcomes factual incorrectness. The AI capabilities the platform offers help our customers create customized and context-specific content that meets their brand guidelines and needs,” said Ramamurthy. MongoDB Atlas Vector Search enables Contentstack to transform content and bring contextual relevance to retrievals. This helps reduce errors caused by large language model hallucinations, allowing the retrieval-augmented generation (RAG) application to deliver better results to users. AppViewX: unlocking scale for a growing cybersecurity SaaS pioneer AppViewX delivers a platform for organizations to manage a range of cybersecurity capabilities, such as certificate lifecycle management and public key infrastructure. The company ensures end-to-end security compliance and data integrity for large enterprises across industries like banking, healthcare, and automotive. Speaking at MongoDB.local Bengaluru in 2024, Karthik Kannan, Vice President of Product Management at AppViewX, explained how AppViewX transitioned from an on-premise product to a SaaS platform in 2021. MongoDB Atlas powered this transition. MongoDB Atlas's unique flexibility, scalability, and multi-cloud capabilities enabled AppViewX to easily manage fast-growing data sets, authentication, and encryption from its customers’ endpoints, device identities, workload identities, user identities, and more. Furthermore, MongoDB provides AppViewX with robust security guaranteeing critical data protection, and compliance. “We've been really able to grow fast and at scale across different regions, gaining market share,” said Kannan. “Our engineering team loves MongoDB,” added Kannan. “The support that we get from MongoDB allowed us to get into different regions, penetrate new markets to grow at scale, so this is a really important partnership that helped us get to where we are.” Zluri Streamlines SaaS Management with MongoDB Zluri provides a unified SaaS management platform that helps IT and security teams manage applications across the organization. The platform provides detailed insights into application usage, license optimization, security risks, and cost savings opportunities. Zluri processes massive volumes of unstructured data—around 9 petabytes per month—from over 800 native integrations with platforms like single sign-on, human resources management systems, and Google Workspace. One of its challenges was to automate the discovery and data analysis across those platforms, as opposed to employing an exhaustive time and labour intensive manual approach. MongoDB Atlas has allowed Zluri to ingest, normalize, process, and manage the high volume and complexity of data seamlessly across diverse sources. “We wanted to connect with every single system that's currently available, get all that data, process all that data so that the system works on autopilot mode, so that you're not manually adding all that information,” said Chaithaniya Yambari, Zluri’s Co-Founder and Chief Technology Officer, when speaking at MongoDB.local Bengaluru in 2024 . As a fully managed database, MongoDB Atlas platform allows Zluri to eliminate maintenance overhead, so its team of engineers and developers can focus on innovation. Zluri also utilizes MongoDB Atlas Search to perform real-time queries, filtering, and ranking of metadata. This eliminates the challenges of synchronizing separate search solutions with the database, ensuring IT managers get fast, accurate, and up-to-date results. These are just a few examples of how MongoDB’s is working with ISVs to shape the future of India’s digital economy. As technology continues to evolve, the role of ISVs in fostering innovation and economic growth will become ever more integral. MongoDB is committed to providing ISVs with a robust, flexible, and scalable database that removes barriers to growth and the ability to innovate. Visit our product page to learn more about MongoDB Atlas. Learn more about MongoDB Atlas Search on our product details page. Check out our Quick Start Guide to get started with MongoDB Atlas Vector Search today.
The Ditto MongoDB Connector: Seamlessly Sync Edge and Cloud Data
Picture a delivery service that uses mobile devices for real-time tracking of packages, routes, and customer interactions. The mobile app on drivers' phones must sync with a cloud database (such as MongoDB Atlas ) to update customer addresses, schedules, and tracking info for backend processing. In remote areas with poor internet, instant syncing might not be possible, leading to inconsistencies and delays if field data doesn’t sync immediately. Simultaneous updates to both the mobile app and the cloud could also cause data conflicts, resulting in confusion and potential data loss. And this is just one example from hundreds. In the logistics & supply chain industry, companies depend on a vast array of edge sensors (like temperature-controlled shipping containers or GPS trackers) and need constant, real-time data updates. In the healthcare industry, patient data collected from wearables or remote monitoring devices must be instantly synced with cloud-based databases for analysis. In the finance industry, mobile banking and real-time trading platforms require instant data synchronization with cloud databases without delays for accurate decision-making. In sum, syncing mobile data in real-world environments is a challenge across industries. The solution: Ditto’s bidirectional connector for MongoDB To address these challenges, we’re excited to introduce the Ditto MongoDB Connector. This solution, a collaboration between Ditto and MongoDB, delivers robust, real-time, bidirectional data synchronization between local apps, Ditto Big Peer , a centralized synchronization engine, and MongoDB Atlas , a robust cloud-based non-relational database. By integrating these technologies, applications can operate effectively even when offline and synchronize changes into the cloud once connectivity is restored. This enhances user satisfaction and fosters trust in digital products. Seamless architecture for real-time synchronization Once the Ditto SDK is integrated into your application, each device running your app maintains a local database. Additionally, these devices automatically form a peer-to-peer (P2P) mesh network and synchronize using available transport protocols such as Bluetooth Low Energy (LE), Local Area Network (LAN), or peer-to-peer Wi-Fi (P2P). The Small Peers connect to the Ditto Big Peer, which serves as a centralized cloud datastore and synchronization engine. The Ditto MongoDB Connector works in tandem with Ditto Big Peer, facilitating data synchronization between a MongoDB Atlas cluster and Ditto Big Peer. It efficiently handles this synchronization by utilizing Conflict-free Replicated Data Types (CRDTs) to resolve conflicts and update both systems with accurate data, preserving changes even when documents are updated simultaneously in both MongoDB Atlas and Ditto. The data between devices stream through Ditto Big Peer using the Ditto MongoDB Connector before being stored in a MongoDB Atlas cluster. Conversely, any changes in a cluster are shared with devices via Ditto Big Peer. Figure 1. Distributed Data Synchronization Architecture for MongoDB Atlas and Ditto Big Peer. Telemetry data handling Real-time information flow is essential across various edge use cases, whether it involves synchronizing telemetry data from edge devices to the cloud or distributing user-initiated updates across devices. The Ditto MongoDB Connector ensures continuous telemetry data synchronization with minimal latency, thanks to a strategy of transferring only deltas—small changes—rather than entire datasets. This significantly improves synchronization speed and reduces data transfer, resulting in fast and seamless updates. Offline-first capabilities: Critical for Field Operations When a device loses connection to the cloud due to poor network conditions or intermittent connectivity, the Ditto MongoDB Connector ensures that changes made locally on the device are queued up and synced back to MongoDB once connectivity is restored. This guarantees data is never lost and ensures consistency across all devices and cloud systems. Robust data security and compliance Security and compliance are critical for modern enterprises, especially those handling sensitive data. By utilizing MongoDB Atlas, audited as SOC 2 Type 2 compliant , the Ditto MongoDB Connector assures data integrity and security across its applications. Whether in healthcare, finance, or other data-sensitive industries, organizations can trust that their data processes meet stringent regulatory standards. How it works in action Let’s come back to our example of a delivery driver updating a customer’s address on their mobile app. This change must be reflected not only on the driver’s device but also in the cloud-based MongoDB database. Ditto’s Bidirectional Connector ensures that updates are instantly synced both ways—any data entered or modified in the mobile app is mirrored in MongoDB, and vice versa. This guarantees consistency across all devices and cloud-based systems. Another example is a salesperson who often finds himself in areas with limited or no network connectivity. With Ditto, mobile apps can continue functioning even offline—storing data locally on the device. Once connectivity is restored, the changes are automatically synced with MongoDB Atlas, ensuring that the cloud database is updated without the risk of data loss or corruption. A seamless integration for developers and enterprises Integrating Ditto’s Bidirectional Connector for MongoDB into your system is simple and designed for scalability. Whether you’re building a new IoT solution or enhancing an existing one, this connector helps solve common data synchronization challenges with ease. Developers will appreciate the simplicity of use and flexibility of the connector, which automatically handles data synchronization and conflict resolution. Enterprises can rely on the scalability and robustness of the solution to ensure their edge devices are always connected, even in remote or low-network environments. Begin your journey The Ditto MongoDB Connector is now available and ready for integration into your systems! Whether you’re tackling edge-to-cloud data challenges in manufacturing, healthcare, or logistics, this new connector simplifies your workflow and ensures data consistency, even in the most demanding environments. Data integration between edge devices and the cloud doesn’t have to be complicated. With Ditto’s new Bidirectional Connector for MongoDB , you can ensure real-time, conflict-free synchronization across all your systems—empowering your business to make better, faster decisions with accurate data, wherever it resides. Want to learn more? Explore Ditto’s comprehensive migration guide to discover how Ditto and MongoDB Atlas enable edge data synchronization. Begin your journey with us today! Read more about Ditto on its MongoDB partner ecosystem page . Sign up for a proof-of-concept .
Securing Digital Transformation with MongoDB and RegData
Data security and privacy have long been paramount to the financial industry, but they are especially critical for institutions undergoing digital transformations or those implementing new technology. For example, the integration of artificial intelligence (AI) and machine learning (ML) into organizations’ infrastructure and offerings introduces security and privacy complexities, making it all the more essential for financial organizations to safeguard sensitive information while complying with regulations. The consequences of a data breach are extensive and significantly impactful. These incidents have transformed from simple cybersecurity concerns into catalysts for financial losses, reputational harm, legal challenges, regulatory penalties, and a significant decline in consumer trust. Even with an increased focus on data security, organizations must adopt modern data architecture to effectively mitigate these risks. For example, using a database solution like MongoDB with built-in encryption, role-based access control, and audit logging can help organizations safeguard sensitive data and respond proactively to potential vulnerabilities. Check out our AI Learning Hub to learn more about building AI-powered apps with MongoDB. The challenge of data security in finance Financial institutions face numerous challenges in protecting data integrity during modernization efforts. The increasing sophistication of cyberattacks, coupled with the need to comply with evolving regulations like the General Data Protection Regulation (GDPR) and the Digital Operational Resilience Act (DORA), creates a complex environment for data management. Institutions must also navigate technical sprawl, where diverse applications and data management systems complicate compliance and operational efficiency. Addressing these challenges requires a holistic approach that integrates data protection into the core design of digital transformation initiatives. Financial institutions need to adopt robust data management practices, ensure the encryption of sensitive data, and maintain vigilant cybersecurity measures. Collaboration with trusted third-party vendors, adopting a privacy-first strategy, and complying with global data protection regulations are essential steps toward safeguarding data privacy in this rapidly evolving digital landscape. Discover how the RegData Protection Suite (RPS), built on MongoDB , enables you to balance technological advancement with regulatory requirements. The solution: MongoDB and RegData MongoDB offers unparalleled reliability, scalability, and flexibility, making it an ideal choice for financial services. MongoDB enables financial institutions to combine operational and AI data in a unified interface and can be deployed on-premises with Enterprise Advanced or across any major cloud provider with MongoDB Atlas , multi-cloud, and hybrid cloud when needed. When combined with RegData's Protection Suite (RPS), organizations can effectively tackle the challenges of digital transformation. RPS is a cloud-native application security platform designed to protect sensitive data through advanced techniques such as encryption, anonymization, and tokenization. Figure 1. Simplified architecture of the RPS solution. Key Features of RegData Protection Suite: Core Configuration: Provides services and a user interface to configure the protection of data. RPS Engine: A sophisticated core engine equipped with various data protection tools. This module is the heart of the application and is responsible for all data protection. Consists of encryption, anonymization, tokenization, and pseudonymization RPS Reporting: A vital component focused on data protection oversight. It gathers and analyzes information on the business application activities protected by RPS to generate a range of valuable reports RPS Manager: Provides end-to-end monitoring capabilities for the components of the RPS platform. RPS Integration: RPS seamlessly integrates with various applications, ensuring that sensitive data is protected across diverse environments. The synergy between MongoDB and RegData shines through in practical applications. For instance, a private bank can leverage hybrid cloud deployments to modernize its operations while maintaining data security. By utilizing RPS, the bank can protect sensitive information during cloud migrations and ensure compliance with regulatory requirements. Additionally, as financial institutions explore outsourcing, RPS helps mitigate risks by anonymizing sensitive data, allowing organizations to maintain control over their data even when leveraging external service providers. Embracing a zero-trust approach for gen AI applications With the rise of AI (and particularly gen AI), banks are developing increasingly more AI- and gen AI-powered applications. While on-premise AI/gen AI model development and testing provides a high level of data security and confidentiality, it may not be within the bank’s budget to afford a production-grade GPU compute pool or one that is large enough to offer sufficient scalability and economy of scale. With this dilemma, banks have begun developing models in private clouds and then deploying on the public cloud to leverage its scalability and economy of scale. MongoDB can serve as that unified operational data layer for a variety of data sources, structured, semi-structured, or unstructured that may also come in different forms (eg. tabular, geospatial, network graph, time series, etc.) for the model development, training, fine-tuning and/or testing. When the model is tested and found to be working, it can then be deployed to the public cloud to serve the AI/gen AI applications. The figure below shows the high-level architecture of how a private bank implemented its gen AI application with MongoDB and RPS. Figure 2. Gen AI data flow architecture focused on data protection. The road to modernization As financial institutions navigate the complexities of digital transformation, the partnership between MongoDB and RegData offers a robust solution for securing data. By adopting a comprehensive data protection strategy, organizations can innovate confidently while ensuring compliance with regulatory standards. Embracing these technologies not only enhances data security but also paves the way for a more resilient and agile financial sector. Establishing a robust data architecture with a modern data platform like MongoDB Atlas enables financial institutions to effectively modernize by consolidating and analyzing data in any format in real-time, driving value-added services and features to consumers while ensuring privacy and security concerns are adequately addressed with built-in security controls across all data. Whether managed in a customer environment or through MongoDB Atlas, a fully managed cloud service, MongoDB ensures robust security with features such as authentication (single sign-on and multi-factor authentication), role-based access controls, and comprehensive data encryption. These security measures act as a safeguard for sensitive financial data, mitigating the risk of unauthorized access from external parties and providing organizations with the confidence to embrace AI and ML technologies. Are you prepared to harness these capabilities for your projects or have any questions about this? Then please reach out to us at industry.solutions@mongodb.com or nfo@regdata.ch . You can also take a look at the following resources: RegData & MongoDB: Securing Digital Transformation Streamline Data Control and Compliance with RegData & MongoDB Implementing an Operational Data Layer Want to learn more about why MongoDB is the best choice for supporting modern AI applications? Check out our on-demand webinar, “ Comparing PostgreSQL vs. MongoDB: Which is Better for AI Workloads? ” presented by MongoDB Field CTO, Rick Houlihan.
Empower Financial Services Developers with the Document Model
In financial services , having a proper data modeling strategy is crucial. The volume of data that banks collect and create is continuously growing, partly due to the expansion of digital banking and payments. Financial institutions rely heavily on data to power applications, analyze risks, and make decisions. The way data is modeled can significantly impact the performance, scalability, and accuracy of these systems, as well as the speed of development for new financial products or services. According to Celent, 62% of banks say the “competitive threat from fintechs and other challengers is increasing.” This is no surprise, as the convenience of managing all financial affairs in one centralized app or on a digital-first platform is pulling more and more customers away from traditional financial institutions. In this blog, we will explore why 24% of retail banks ranked “data platforms and management” as one of their top three IT spending priorities for 2024–2025 and how data modeling plays a critical role in this strategic focus. Data modeling and the document model In financial services, data modeling is a critical process that underpins effective data management, enabling institutions to harness the full potential of their data. This process involves identifying relevant financial data and determining how it should be visualized, including its structure, relationships, and management. A well-structured data model leads to better application performance, simpler implementations, and lower costs. It also enhances adaptability and maintainability over time. The principle that data accessed together should be stored together is fundamental for optimizing performance. MongoDB is a document-oriented NoSQL database that offers flexibility and scalability, making it an ideal choice for the industry. It runs with unparalleled reliability, security, and flexibility in a multi-cloud or on-premises environment. Unlike traditional relational databases, MongoDB allows for a more dynamic schema, enabling organizations to adapt to changing business needs without the constraints of rigid table structures. Document databases have the following key features: Document model: Data is stored in documents (unlike other databases that store data in structures like tables or graphs). The documents map to objects in the most popular programming languages, enabling developers to rapidly develop their applications. Flexible schema: MongoDB’s design enables developers to store data in a way that reflects the real-world relationships between entities, making it easier to manage complex data structures. Data distribution and resilience: Document databases are distributed, which allows for horizontal scaling (typically cheaper than vertical scaling) and data distribution. Queryability through an API or query language: Document databases have an API or query language that allows developers to execute the CRUD operations on the database. Developers have the ability to query for documents based on unique identifiers or field values. MongoDB’s document model enables an organization to design document structures that mirror its application’s access patterns. By embedding related data as subdocuments and arrays within a single document, it can make sure that data that is frequently accessed together is stored together. This alignment simplifies the mapping between the application and the database, enhancing both development efficiency and performance. In contrast to relational databases, document databases are better suited to the needs of modern applications because of their ability to store diverse data types (both structured and unstructured) in binary JSON (BSON) format. This flexibility essentially eliminates the middle layer necessary to convert to a SQL-like format, resulting in easier-to-maintain applications, lower development times, and faster responses to changes. As a document’s schema is dynamic and self-describing, developers don’t need to predefine it in the database. They can modify it at any time, avoiding disruptive schema migrations and improving their own productivity and experience. Watch now: Intro to Data Modeling for Financial Services and Insurance explains: The definition of data modeling Relational database management systems vs MongoDB Relationships: Linking vs Embedding Design Patterns MongoDB’s document model for financial services Leading financial institutions across the world are increasingly adopting MongoDB with seven of the top ten banks in the world (per the 2024 Forbes Global 2000) utilizing MongoDB in their application architectures. MongoDB Atlas is an integrated suite of data services centered on a document database designed to accelerate and simplify how users build with data. MongoDB enables banks to employ an iterative approach to banking modernization . This approach preserves legacy components for as long as they’re needed. By deploying MongoDB as an operational data layer (ODL) in a phased approach, banks can embark on their digital transformation journeys iteratively, without the risk of an all-or-nothing, rip-and-replace approach. Once the new architecture is in place, development teams can build new business functionality faster and scale new services to millions of users. Here are some of the ways that MongoDB helps financial institutions meet their industry data challenges: Open finance: MongoDB’s flexible schema, with the ability to handle various data types including structured and unstructured data, seamlessly integrates with modern technologies and frameworks, making it a great fit for orchestrating open API ecosystems. Personalized banking experiences: Retail banks aim to deliver hyper-personalized services, such as tailored financial advice or customized product recommendations. A flexible and accurate document model ensures platforms can integrate data from multiple channels (mobile apps, branches, and ATMs) for a seamless experience. Encryption and access control: MongoDB provides security capabilities like field-level encryption, role-based access control (RBAC), and auditing—key features to enhance data security and facilitate compliance with regulations like Financial Data Access (FiDA). By encrypting sensitive customer data both at rest and in transit, MongoDB helps keep data secure and tamper-proof throughout its lifecycle. Data sovereignty and global deployment: Financial data must adhere to strict geographic regulations, with certain jurisdictions enforcing the requirement that data remain within specific regions. MongoDB Atlas offers multi-cloud and multi-region deployments, enabling financial institutions to store data in specific regions while maintaining compliance with data sovereignty laws. Multidocument ACID transactions: Transactions in MongoDB feel just like transactions developers are familiar with in relational databases. With multi-document atomicity, consistency, isolation, and durability (ACID) transactions, developers can address transactional use cases across multiple documents within the same cluster. Payment system scalability and flexibility: MongoDB’s document database excels in unifying an organization’s data, from backend payment processing to customer interactions, surfacing insights to create a seamless, connected, and personalized customer journey. Payment systems must accommodate fluctuating transaction volumes and evolving business needs. MongoDB Atlas makes scaling as easy as setting the right configuration. It supports both horizontal and vertical scaling. Fraud detection: MongoDB’s document data model incorporates any kind of data—any structure, any format, any source—no matter how often it changes, enabling an organization to create a holistic picture of its customers to better predict transaction anomalies in real-time. MongoDB then enables it to process large amounts of data and analyze it in real-time to identify suspicious activity. Financial data management: MongoDB lets organizations capture and store financial and trade-related data together, respond to compliance and regulatory requests with confidence, and analyze pretrade communications to gain insights and detect errors. AI-driven interactive banking: MongoDB is designed to work seamlessly with leading AI frameworks, enabling banks to integrate and scale AI applications quickly and efficiently. MongoDB’s flexibility supports innovation by providing a scalable, developer-friendly environment that enables businesses to rapidly develop new financial services and products and scale to support millions of users. And, as a document-based database, MongoDB supports the flexible data modeling that is so crucial to the financial services industry. If you have any questions or would like to learn more about MongoDB and data modeling, feel free to check out the following resources: Intro to Data Modeling for Financial Services and Insurance Temenos Banking Cloud Scales to Record High Transactions with MongoDB Atlas and Microsoft Azure Our Solutions Library is where you can learn about different use cases for gen AI and other interesting topics that are applied to financial services and many other industries.
Built With MongoDB: Kraken Coding Revolutionizes Clinical Decision Support
Within the world of healthcare, clinicians often find themselves overwhelmed by the sheer volume of information they must process to provide optimal patient care. Text-based guidelines and procedures, while essential, can be cumbersome and difficult to navigate. Recognizing this challenge, John Shanks, co-founder of Kraken Coding , embarked on a mission to transform the way clinical staff access decision support information through its product, Clinical Branches. The birth of clinical branches Kraken Coding began its journey with the “Open Source Antimicrobial Stewardship” project, which aimed to provide decision support pathways for antimicrobial prescribing. Despite its limited functionality, the project gained traction. Early success, coupled with valuable feedback from users, inspired the team to develop a more robust solution using a modern database platform, MongoDB. The initial vision for Clinical Branches was simple yet powerful: to create a tool that would streamline access to critical clinical information. Shanks, who developed the original tool during his tenure as a hospital pharmacist, understood firsthand the challenges posed by high staff turnover and the difficulty of training new employees. Clinicians were inundated with lengthy documents that often went unread, leading to potential risks in patient care. The goal was to create a structured decision tree that would guide clinicians through the decision-making process quickly and efficiently. A unique approach to decision support What sets Kraken Coding apart from other solutions in the healthcare industry is its innovative use of data generated through the decision-making process. By abstracting text-based procedures into semi-structured decision algorithms, Clinical Branches not only provide immediate guidance but also collect valuable data that can be analyzed for continuous improvement. The team is currently working on an exciting project supported by the UNSW Tyree IHealthE Innovation Catalyst Awards, which aims to convert this data into semantic embeddings. By utilizing vector search technology, they hope to predict risks for patient admissions early in the patient journey. The role of MongoDB in scaling success A crucial element of Kraken Coding’s growth has been its partnership with MongoDB Atlas . Shanks initially chose MongoDB due to its simplicity and scalability. At university, the practical exercises and tutorials involved mostly SQL inner joins and database normalization. Any courses that did cover NoSQL did so only superficially, but after doing an online course using MongoDB, he saw how easy it was to set up an application and was happy to ditch SQL. With Clinical Branches, he knew MongoDB would be the perfect choice. The focus on simplicity and scalability aligned perfectly with the startup’s needs. As the company expanded its operations, particularly with the recent deployment of a new application server in Canada, MongoDB’s capabilities made it easy to add nodes and tackle data sovereignty issues without compromising sensitive information. Without MongoDB, the team would have to manually set up a database instance for a new geographic location, which can involve complex configurations to manage consistency, conflict resolution, and data synchronization across regions. With MongoDB, they can deploy to any region in Canada with a few clicks. One of the standout features that Kraken Coding has benefited from is MongoDB Atlas Vector Search , which has already shown promise in predicting optimal drug dosing based on historical patient data. Support from the MongoDB for Startups program The MongoDB for Startups program has played a pivotal role in Kraken Coding’s development. The Atlas credits provided financial relief for the early-stage venture, allowing the team to focus on building their product without the burden of high operational costs. Additionally, the free technical advice they received during the initial phases of their project was invaluable. A two-day technical consultant review ensured that their database indexing, design, and application routers were correctly configured before going live, saving the team about 50 hours of rework, by Shanks’ estimate, and ensuring the platform was scalable. Looking ahead: The future of Clinical Branches As Kraken Coding continues to innovate, the roadmap for the coming year is filled with exciting developments. The team is preparing to start phase one audits to register their software as a medical device, focusing on Bayesian inference and vector search to support optimal drug dosing strategies. They are also launching a new project that will utilize pathway answers in conjunction with patient information to predict likely patient outcomes. With their first pilot in Canada on the horizon, the future looks bright for Kraken Coding. Kraken Coding is at the forefront of revolutionizing clinical decision support through its innovative product, Clinical Branches. By leveraging modern technology and data-driven insights, the startup is not only improving the efficiency of clinical workflows but also enhancing patient care. If you’re working in healthcare and looking for a simple initiative that can have a big impact for your service, check out Kraken Coding . To learn more and get started with MongoDB Vector Search, visit our Vector Search Quick Start guide . Are you part of a startup and interested in joining the MongoDB for Startups program? Apply now .
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.