Atlas Stream Processing features

As with the database itself, streaming data requires flexibility, adaptation, and evolution. The document model is the best way to do it.

Easily connect to your key streaming sources and sinks in Kafka and Atlas, and merge data continuously.

The Query API and aggregation framework include extended capabilities for processing complex, continuous data streams.

Create time-based windows, lookups across collections, and complex validation for rich, multi-event processing.

Perform continuous schema validation and detect message corruption or late-arriving data that has missed a processing window.

Continuously materialize views into Atlas collections or streaming systems like Apache Kafka to maintain fresh analytical views.

Checkpoints capture the state of operations after completion. Easily restart stream processors after an interruption.

Processing streaming data can be opaque. Use .process() to iteratively explore new stream processing pipelines and build quickly.

Build out your stream processing infrastructure via the MongoDB shell or automate your stream processors using Terraform.