New capabilities for MongoDB platform unveiled

Author: Simon Rowley

MongoDB has announced new capabilities for its MongoDB Atlas platform that make it faster and easier to build, deploy, and run modern applications with the performance and scale organisations require.

MongoDB Atlas is one of the most widely distributed developer data platforms in the world, and tens of thousands of customers and millions of developers rely on its operational database and integrated data services to power business-critical applications across cloud providers. The general availability of MongoDB Atlas Stream Processing makes it easier to use real-time data from a wide variety of sources to run highly responsive applications.

MongoDB Atlas Search Nodes on Microsoft Azure give organisations more flexibility for optimising the performance and cost of generative AI workloads that drive intelligent applications at scale. MongoDB Atlas Edge Server reduces the complexity of managing data for distributed applications that span locations from the cloud to on premises to devices at the edge.

“Customers tell us they love MongoDB Atlas because it provides an integrated set of capabilities on one platform that can store and process their organisation’s operational data across all of their applications,” says Sahir Azam, Chief Product Officer at MongoDB.

“Customers also tell us that MongoDB’s highly flexible and scalable document data model is a perfect fit for powering modern applications that can take advantage of generative AI and their real-time proprietary data. The additional services we’re launching today for MongoDB Atlas not only make it easier to build, deploy, and run modern applications, but also make it easier to optimise performance while reducing costs.”

The new MongoDB Atlas capabilities announced today enable organisations of all sizes across industries to build, deploy, and run next-generation applications with the security, resiliency, and durability today’s business environment demands. Specifically, they can now:

Simplify building highly responsive applications with streaming data. Now generally available, MongoDB Atlas Stream Processing enables developers to take advantage of data in motion and data at rest to power event-driven applications that can respond to changing conditions. Streaming data – coming from sources like IoT devices, customer browsing behaviours, and inventory feeds – is critical to modern applications because it allows organisations to create dynamic experiences as end-user behaviours or conditions change.

However, streaming data is highly dynamic, and inflexible data models are not ideal for building event-driven applications that need to continuously adjust to the real world. Because it is built on a flexible and scalable data model, MongoDB Atlas Stream Processing allows organisations to build applications that analyse data in motion and at rest and make adjustments to business logic in seconds.

For example, organisations can build applications that dynamically optimise shipping routes based on weather conditions and supply chain data feeds, or can continuously analyse financial transaction data feeds and purchase histories for AI-powered fraud detection in near-real time. By using MongoDB Atlas Stream Processing, organisations can do more with their data in less time and with less operational overhead.

Optimise the performance and efficiency of generative AI applications. MongoDB Atlas Search Nodes – generally available on AWS and Google Cloud, and now in preview on Microsoft Azure – provide dedicated infrastructure for generative AI and relevance-based search workloads that use MongoDB Atlas Vector Search and MongoDB Atlas Search. MongoDB Atlas Search Nodes are independent of core operational database nodes and allow customers to isolate workloads, optimise costs, and reduce query times by up to 60%.

In addition to helping optimise performance and cost, MongoDB Atlas Search Nodes enable organisations to run highly available generative AI and relevance-based search workloads at scale for the most demanding applications. For example, an airline company can use MongoDB Atlas Search Nodes to optimise the performance and scale an AI-powered booking agent experiencing a surge in usage by seamlessly isolating the vector search workload and scaling the required infrastructure – without resizing the required compute or memory resources for their operational database workload.

Deploy applications that seamlessly connect from the cloud to the edge. Now available in public preview, MongoDB Atlas Edge Server gives developers the capability to deploy and operate distributed applications in the cloud and at the edge. MongoDB Atlas Edge Server provides a local instance of MongoDB with a synchronisation server that runs on local or remote infrastructure and significantly reduces the complexity and risk involved in managing applications in edge environments.

With MongoDB Atlas Edge Server, applications can access operational data even with intermittent connections to the cloud. For example, a hospital system can use MongoDB Atlas Edge Server to help enable applications running on patient healthcare devices to remain functional during power outages and connectivity disruptions. With Atlas Edge Server, their data will automatically synchronise once connectivity is restored.

MongoDB Atlas Edge Server also supports data tiering to prioritise the synchronisation of critical data to the cloud, reducing network congestion. And, MongoDB Atlas Edge Server maintains a local data layer to reduce latency and enable faster actions based on real-time data. With MongoDB Atlas Edge Server, organisations can seamlessly run highly available, modern applications closer to end-users with less complexity.

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