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The new Atlas features are aimed at making it faster and easier to build, deploy and run applications.

This is done via MongoDB Atlas Stream Processing, available immediately. It allows developers to use data in motion and data at rest for event-driven applications that respond to changing conditions. MongoDB sees streaming data, in particular, as critical information for modern applications because it can be used to respond to a user’s new behaviour. Yet, it can be difficult for developers to use such data for apps.

A flexible and scalable data model from Atlas Stream Processing should change that, by analyzing data in motion and data at rest and making adjustments to business logic within seconds. To illustrate, an app can be built that dynamically adjusts a product’s route based on weather conditions and data from the supply chain.

Generative AI app optimization

MongoDB Atlas Search Nodes represents the second update to advance application development. With them, the database provides a “dedicated infrastructure for generative AI and relevance-based search workloads.” These Atlas Search Nodes are independent of Atlas’ operational database nodes, which allows developers to isolate workloads, optimize costs, and reduce query times.

As an example, MongoDB cites an airline application where the Atlas Search Nodes optimize the performance of an AI-powered booking agent. This booking agent experienced a spike in usage. With Atlas Search Nodes, it is possible to isolate the vector search workload and scale the required infrastructure, without adjusting the size of the compute and memory resources required for the operational database workload.

Atlas Search Nodes is available immediately on AWS and Google Cloud. For Microsoft Azure, a preview is still in effect.

Tip: MongoDB Queryable Encryption protects sensitive data, even when it’s in use