Ventana Research Analyst Perspectives

I previously wrote about how document-database providers have added support for ACID transactions and the SQL query language, making their products increasingly suitable for use as replacements for applications that previously depended on relational databases. Adoption of non-relational NoSQL databases is by no means reliant on displacing incumbent relational databases, and initial adoption is often driven by differentiating capabilities, such as developer agility and application flexibility. However, relational compatibility is increasingly important to document database vendors as they try to encourage expanded adoption by customers at the expense of incumbent providers. In the context of larger transformation and modernization efforts, increasing adoption requires vendors such as MongoDB to not only provide support for new technologies such as generative AI but also compatibility with key relational database features and the ability to facilitate migration from the relational model.  

Founded in 2007, MongoDB has established itself as one of the most prominent NoSQL database providers with its document-oriented database and associated cloud services, as well as a focus on the needs of development teams to deliver innovation through the creation of data-driven applications. Although MongoDB continues to make its software available for self-managed deployment on premises or in the cloud, the company’s success is increasingly built on its MongoDB Atlas managed service, which is available on Amazon Web Services, Google Cloud Platform and Microsoft Azure. The proportion of MongoDB’s revenue associated with Atlas has been steadily increasing since it was launched in 2016 and was responsible for almost two-thirds (63%) of the company’s total revenue of $423.8 million in the second quarter of fiscal 2024. MongoDB’s total second-quarter revenue grew 40% driven by the role the document database provides in supporting agile development and business flexibility. This is attributable to the underlying document database model which does not require a strict database schema, enabling the data model to evolve as application requirements change.  

The flexibility of the document model has been key to developer-led adoption of MongoDB, with the database primarily being used to support the development and deployment of net-new applications rather than as a direct replacement for relational databases. This trend can be expected to continue. I assert that through 2026, emerging relational and non-relational database providers will primarily be adopted for new applications with incumbent relational database vendors deployed for the majority of existing operational workloads. However, MongoDB has also recently introduced its MongoDB Relational Migrator tool, stepping up its ability to facilitate the migration of applications from existing databases as organizations accelerate modernization projects to support new technologies such as generative AI. 

MongoDB Relational Migrator is designed to reduce the complexity associated with application migration and transformation from relational to document-based data models by analyzing existing databases and automatically generating the data schema and code required to transform and migrate the data to MongoDB Atlas. Although customers are advised to run the updated application in a testing environment to ensure it is operating as intended before deploying it to production, MongoDB Relational Migrator should save users time and manual effort. In September the company announced an update to the tool, including the use of generative AI to convert SQL queries and stored procedures to MongoDB Query API syntax. That was one of multiple enhancements recently announced by MongoDB related to the use of generative AI, with the company also announcing the ability to convert natural language queries to the MongoDB Query API syntax using its MongoDB Compass user interface, as well as a natural language interface for its MongoDB Atlas Charts data visualization tool and a chatbot interface to the MongoDB Documentation. 

The latter takes advantage of MongoDB Atlas Vector Search, which was launched in June to enable developers to convert data stored in MongoDB into vector embeddings that can be used to augment large language and other generative AI models with enterprise information and data. As I recently described, vector search and retrieval-augmented generation (RAG) can help organizations improve trust in the output of generative AI by providing access to factually accurate and up-to-date information. I assert that through 2026, almost all organizations developing applications based on generative AI will explore vector search and RAG to complement foundation models with proprietary data and content.  

Other recent MongoDB enhancements announced by the company include MongoDB Atlas Stream Processing for continual processing of streams of event data, the ability to isolate and independently scale search workloads with MongoDB Atlas Search Nodes and scalability improvements for MongoDB Time Series. The company also recently announced an increased focus on edge data processing with the launch of MongoDB Atlas at the Edge. The new offering better enables organizations to support local data processing on mobile and IoT devices as well as in remote data centers or disconnected infrastructure. MongoDB Atlas at the Edge includes Atlas Edge Server, a containerized MongoDB server instance for deployment in edge locations that synchronizes with the MongoDB Atlas cloud service, and also Atlas Device Sync which provides synchronization with devices and equipment running the Atlas Device SDK (formerly known as Realm). MongoDB has also recently announced an initiative to drive adoption in key industry verticals including financial services, manufacturing, automotive, healthcare and insurance with domain-specific accelerators as well as targeted support and services.  

MongoDB is now well-established as a mainstream database provider, but the number and range of recent announcements are indicative of a company that continues to mature both its product portfolio and its approach to the market. Support for generative AI is nascent, which is true for all database providers given the speed at which it has impacted the entire technology industry, but MongoDB Atlas Vector search is indicative of how the company can support the adoption of generative AI, while MongoDB Relational Migrator illustrates how generative AI can help MongoDB and its customers accelerate database migration and transformation initiatives. I recommend that organizations evaluating potential database providers for new development and transformation projects include MongoDB Atlas in their evaluations.

Regards,

Matt Aslett