MongoDB said today it’s beefing up its cloud database MongoDB Atlas with a series of new products and features that it says will make it easier for developers to build modern applications.

They include new generative artificial intelligence capabilities for MongoDB’s Vector Search feature, enabling the retrieval and personalization of highly relevant information, Search Nodes for dedicated resources with search workloads at scale and Stream Processing high-velocity streams of data.

The new capabilities in MongoDB Atlas were announced at the company’s annual developer conference MongoDB.local NYC, alongside a bunch of other updates. Also new today is MongoDB Relational Migrator, a tool that simplifies application migration, and a new partnership with Google Cloud that’s intended to accelerate the use of generative AI to build new types of applications.

MongoDB is the creator of the document-oriented MongoDB database, which is used for a wide range of data-intensive applications and prized for its ability to store information in multiple different formats. MongoDB Atlas is the cloud-hosted version of that database.

The company explained that the choice of database is critical for enterprises that want to take advantage of generative AI, which is the technology that sits at the heart of next-generation chatbots such as OpenAI LP’s ChatGPT. It said a database that’s unified, fully managed, flexible and scalable makes life much easier for developer teams.

Today’s updates mean that MongoDB Atlas is positioned to become that database, the company said. Its updated Vector Search feature is key to building generative AI apps, as they require data to be stored in “vectors,” or geometric representations.

Generative AI models work by measuring the similarity between vectors to construct sentences or images probabilistically from prompts or return accurate search results with more context than traditional search engines. With MongoDB Atlas Vector Search, companies can support new workloads such as semantic search, text-to-image search and personalized recommendation systems.

MongoDB Atlas Vector Search also makes it possible to augment the capabilities of existing generative AI models with additional data to create more accurate results in specific use cases and domains, the company said.

Constellation Research Inc. analyst Doug Henschen told SiliconANGLE that vector search is a key enabler of generative AI workloads. “Vectors are geometric/numerical representations of semantically similar text, images, audio and other content, so the possibilities for helping to train custom large language models with your own data are limitless,” he pointed out. “Vectors could help to improve natural language query, NL text/image/code generation and more.”

Meanwhile, MongoDB Atlas Stream processing is a key update that makes it easier to process streaming data and support real-time applications. In Henschen’s view, this is the most important update for customers, as low-latency workloads are becoming more and more prevalent for enterprises.

“MongoDB really had to step up on this front to live up to its billing as the developer data platform,” the analyst said. “Rival data platforms associated with analytics, such as Snowflake and Databricks, have already addressed real-time needs, so MongoDB is filling a competitive gap.

There’s also greater scalability with Time Series Collections, which make it easier to handle time-series workloads with the ability to modify such data after it has been ingested. That’s important because time series databases typically do not allow for data to be modified after it has been created, even when errors have occurred.

A final update pertains to the ability to tier and query data on Microsoft Azure with MongoDB Atlas Online Archive and Atlas Data Federation. With this, customers can tie their MongoDB databases to the most cost-effective cloud storage tier, while retaining the ability to query with higher performance.

MongoDB Chief Executive Dev Ittycheria (pictured) said the new features were prioritized based on customer feedback. “We’re further supporting customers running the largest, most demanding and mission-critical workloads that require continually increasing scalability and flexibility,” he said.

Migrations and integrations

The new MongoDB Relational Migrator tool is designed to help developers that want to migrate their existing applications from a legacy database to MongoDB, and is promised to do this in a cost-effective way with zero risk.

Meanwhile, the new partnership with Google Cloud pertains to MongoDB’s integration with Vertex AI, which is a suite of tools for data scientists to build, automate, standardize and manage machine learning projects, including generative AI models.

Ittycheria said the shift to generative AI begins first and foremost with developers, who are the ones tasked with integrating the technology into a new class of business applications. “We want to democratize access to game-changing technology so all developers can build the next big thing,” the CEO said. “With our strategic partnership with Google Cloud, it’s now easier for organizations of all shapes and sizes to incorporate AI into their applications.”

Henschen said the integration with Google Cloud plays to MongoDB’s new vector search capabilities. “MongoDB can now provide the data expressed as vectors to drive large language model training, and Vertex AI is one of the many tools available for developing custom LLMs,” he explained.

New verticals

The flurry of updates continued with the announcement of MongoDB Atlas for Industries, which was billed as a new program for organizations to accelerate cloud adoption and modernization through industry-specific expertise and integrated solutions. The idea is to provide customers with access to expert-led architectural design reviews, knowledge accelerators to provide more relevant training for developer teams, and partnerships to build solutions to industry-specific challenges, MongoDB said.

MongoDB Atlas for Industries sees the company launch its first-ever vertical offerings for financial services customers, and there are more to come in manufacturing, automotive, insurance, healthcare and retail later this year.

Like with the stream processing updates, MongoDB’s new support for industry verticals follows in the footsteps of its competitors, namely Amazon Web Services Inc., Google Cloud, Snowflake and Databricks, Henschen pointed out. “MongoDB is starting with the one industry that’s on everyone’s shortlist, financial services, because that’s where the most money promises to be,” he added. “The typical payoff for vertical industry clouds is prebuilt solutions and short cuts for common use cases. So far it’s early days, so we’re yet to see what it will offer in terms of speeding time to market and accelerating time to value.”

Finally, the company announced a number of more general updates for its databases, including expanded programming language support for MongoDB Atlas, a move that will simplify the deployment of resources on Amazon Web Services using infrastructure-as-code. There’s also a new Kotlin Driver for MongoDB, which makes it possible to build Kotlin-based applications, and MongoDB Atlas Kubernetes Operator, which simplifies the task of working with containerized applications.

“Everything MongoDB is announcing is designed to make it a more comprehensive and complete ‘developer data platform,'” Henschen said. “The more that MongoDB can provide to enable developers with all the tools that they need, the stickier the platform becomes for those developers and the organizations they work for.”

Photo: SiliconANGLE