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Dataflow is a managed service for executing a wide variety of data processing patterns. The documentation on this site shows you how to deploy your batch and streaming data processing pipelines using Dataflow, including directions for using service features.

The Apache Beam SDK is an open source programming model that enables you to develop both batch and streaming pipelines. You create your pipelines with an Apache Beam program and then run them on the Dataflow service. The Apache Beam documentation provides in-depth conceptual information and reference material for the Apache Beam programming model, SDKs, and other runners.

To learn basic Apache Beam concepts, see the Tour of Beam and Beam Playground. The Dataflow Cookbook repository also provides ready-to-launch and self-contained pipelines and the most common Dataflow use cases.

Apache, Apache Beam, Beam, the Beam logo, and the Beam firefly mascot are registered trademarks of The Apache Software Foundation in the United States and/or other countries.
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Use case
Use cases

Run HPC highly parallel workloads

With Dataflow, you can run your highly parallel workloads in a single pipeline, improving efficiency and making your workflow easier to manage.

Streaming

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Use case
Use cases

Run inference with Dataflow ML

Dataflow ML lets you use Dataflow to deploy and manage complete machine learning (ML) pipelines. Use ML models to do local and remote inference with batch and streaming pipelines. Use data processing tools to prepare your data for model training and to process the results of the models.

ML Streaming

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Use case
Use cases

Create an ecommerce streaming pipeline

Build an end-to-end ecommerce sample application that streams data from a webstore to BigQuery and Bigtable. The sample application illustrates common use cases and best practices for implementing streaming data analytics and real-time artificial intelligence (AI).

ecommerce Streaming

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