# Dataflow documentation

[Read product documentation](https://docs.cloud.google.com/dataflow/docs/overview)
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](https://beam.apache.org/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](https://tour.beam.apache.org/) and [Beam Playground](https://play.beam.apache.org/).
The [Dataflow Cookbook](https://github.com/GoogleCloudPlatform/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.*

## Documentation resources

Find quickstarts and guides, review key references, and get help with common issues.

### Guides

-

  [Create a Dataflow pipeline using Java](https://docs.cloud.google.com/dataflow/docs/quickstarts/create-pipeline-java)

-

  [Create a Dataflow pipeline using Python](https://docs.cloud.google.com/dataflow/docs/quickstarts/create-pipeline-python)

-

  [Create a Dataflow pipeline using Go](https://docs.cloud.google.com/dataflow/docs/quickstarts/create-pipeline-go)

-

  [Create a streaming pipeline using a Dataflow template](https://docs.cloud.google.com/dataflow/docs/quickstarts/create-streaming-pipeline-template)

-

  [Build and run a Flex Template](https://docs.cloud.google.com/dataflow/docs/guides/templates/using-flex-templates)

-

  [Deploy Dataflow pipelines](https://docs.cloud.google.com/dataflow/docs/guides/deploying-a-pipeline)

-

  [Auto VM Selection](https://docs.cloud.google.com/dataflow/docs/guides/auto-vm-selection)

-

  [Develop with notebooks](https://docs.cloud.google.com/dataflow/docs/guides/interactive-pipeline-development)

-

  [Troubleshooting and debugging](https://docs.cloud.google.com/dataflow/docs/guides/troubleshooting-your-pipeline)

### Reference

-

  [Install or update the Apache Beam SDK](https://docs.cloud.google.com/dataflow/docs/guides/installing-beam-sdk)

-

  [Java SDK](https://beam.apache.org/documentation/sdks/javadoc/current/)

-

  [Python SDK](https://beam.apache.org/documentation/sdks/pydoc/current/)

-

  [Go SDK](https://pkg.go.dev/github.com/apache/beam/sdks/v2/go/pkg/beam)

-

  [SDK version support status](https://docs.cloud.google.com/dataflow/docs/support/sdk-version-support-status)

-

  [REST API](https://docs.cloud.google.com/dataflow/docs/reference/rest)

-

  [gcloud command-line functions](https://docs.cloud.google.com/sdk/gcloud/reference/dataflow)

-

  [Google-provided templates](https://docs.cloud.google.com/dataflow/docs/concepts/dataflow-templates)

### Resources

-

  [Dataflow code samples](https://docs.cloud.google.com/dataflow/docs/samples)

-

  [Pricing](https://docs.cloud.google.com/dataflow/pricing)

-

  [Quotas and limits](https://docs.cloud.google.com/dataflow/quotas)

-

  [Release Notes](https://docs.cloud.google.com/dataflow/docs/release-notes)

-

  [Getting support](https://docs.cloud.google.com/dataflow/docs/support/getting-support)

-

  [Billing questions](https://docs.cloud.google.com/dataflow/docs/support/billing-questions)

## Related resources

Explore self-paced training, use cases, reference architectures, and code samples with examples of how to use and connect Google Cloud services. Use case Use cases

### [Run HPC highly parallel workloads](https://docs.cloud.google.com/dataflow/docs/hpc-ep)


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

Streaming

<br />

Use case Use cases

### [Run inference with Dataflow ML](https://docs.cloud.google.com/dataflow/docs/machine-learning)


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

<br />

Use case Use cases

### [Create an ecommerce streaming pipeline](https://docs.cloud.google.com/dataflow/docs/tutorials/ecommerce-retail-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

<br />

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