PREVIEW
Atlas Stream Processing
Atlas Stream Processing Explained in 3 minutes
Learn how Atlas Stream Processing combines the document model, flexible schemas, and a rich aggregation language to provide a new level of power and convenience when building applications that require processing complex event data at scale.
Stream processing like never before
When working with streaming data, schema management is critical to data correctness and developer productivity. MongoDB’s document model and familiar aggregation framework give developers powerful capabilities and productivity gains you won't find elsewhere in stream processing.
Unifying data in motion and data at rest
For the first time, developers can use one platform – across API, query language, and data model – to continuously process streaming data alongside the critical application data stored in their database.
Fully managed in Atlas
Atlas Stream Processing builds on our robust and integrated developer data platform. With just a few API calls and lines of code, a developer can stand up a stream processor, database, and API serving layer — all fully managed on any of the major cloud providers.Capabilities
Continuous processing
Build aggregation pipelines to continuously query, analyze, and react to streaming data without the delays inherent to batch processing.
Continuous validation
Perform Continuous Schema Validation to check that events are properly formed before processing, detect message corruption, and detect late arriving data that has missed a processing window.
Continuous merge
Continuously materialize views into Atlas database collections or streaming systems like Apache Kafka to maintain fresh analytical views of data supporting decision making and action.
Atlas Stream Processing
Event-driven applications
Paving the path to a responsive and reactive real-time businessCheck out the simplicity and power of Atlas Stream Processing
Querying Apache Kafka data streams
Advanced analytics with windowing functions
Schema validation of complex events
Querying Apache Kafka data streams
Advanced analytics with windowing functions
Schema validation of complex events
FAQ
What is streaming data?
What is stream processing?
How is event streaming different from stream processing?
Streaming data lives inside of event streaming platforms (like Apache Kafka), and these systems are essentially an immutable distributed log. Event data is published and consumed from event streaming platforms using APIs.
Developers need to use a stream processor to perform more advanced processing, such as stateful aggregations, window operations, mutations, and creating materialized views. These are similar to the operations one does when running queries on a database, except that stream processing continuously queries an endless stream of data. This area of streaming is more nascent; however, technologies such as Apache Flink and Spark Streaming are quickly gaining traction.
Stream processing is the area where Atlas Stream Processing focuses. MongoDB is providing developers with a better way to process streams for use in their applications, leveraging the aggregation framework.
Why did MongoDB build Atlas Stream Processing?
How do I get access to the private preview?
How is stream processing different from batch processing?
Stream processing is the processing of data continuously. In the context of building event-driven applications, stream processing enables reactive and compelling experiences like real-time notifications, personalization, route planning, or predictive maintenance.
Batch processing does not work on continuously produced data. Instead, batch processing works by gathering data over a specified period of time and then processing that static data as needed. An example of batch processing is a retail business collecting sales at the close of business each day for reporting purposes and/or updating inventory levels.
Request private preview today
