Apache Beam is a unified model for defining both batch and streaming data-parallel processing pipelines, as well as a set of language-specific SDKs for constructing pipelines and Runners for executing them on distributed processing backends, including Apache Flink, Apache Spark, Google Cloud Dataflow, and Hazelcast Jet.

If you're new to Apache Beam, start here:

1. Choose a language:

   • Java → Java Quickstart
   • Python → Python Quickstart
   • Go → Go Quickstart

2. Run your first example:

   • Minimal WordCount example (available in this repository)

3. Understand core concepts:

   • PCollection
   • PTransform
   • Pipeline

Beam provides a general approach to expressing embarrassingly parallel data processing pipelines and supports three categories of users, each of which have relatively disparate backgrounds and needs.

1. End Users: Writing pipelines with an existing SDK, running it on an existing runner. These users want to focus on writing their application logic and have everything else just work.
2. SDK Writers: Developing a Beam SDK targeted at a specific user community (Java, Python, Scala, Go, R, graphical, etc). These users are language geeks and would prefer to be shielded from all the details of various runners and their implementations.
3. Runner Writers: Have an execution environment for distributed processing and would like to support programs written against the Beam Model. Would prefer to be shielded from details of multiple SDKs.

The model behind Beam evolved from several internal Google data processing projects, including MapReduce, FlumeJava, and Millwheel. This model was originally known as the “Dataflow Model”.

To learn more about the Beam Model (though still under the original name of Dataflow), see the World Beyond Batch: Streaming 101 and Streaming 102 posts on O’Reilly’s Radar site, and the VLDB 2015 paper.

The key concepts in the Beam programming model are:

• PCollection: represents a collection of data, which could be bounded or unbounded in size.
• PTransform: represents a computation that transforms input PCollections into output PCollections.
• Pipeline: manages a directed acyclic graph of PTransforms and PCollections that is ready for execution.
• PipelineRunner: specifies where and how the pipeline should execute.

Beam supports multiple language-specific SDKs for writing pipelines against the Beam Model.

Currently, this repository contains SDKs for Java, Python and Go.

Have ideas for new SDKs or DSLs? See the sdk-ideas label.

• Python SDK readme

Beam supports executing programs on multiple distributed processing backends through PipelineRunners. Currently, the following PipelineRunners are available:

• The DirectRunner runs the pipeline on your local machine.
• The PrismRunner runs the pipeline on your local machine using Beam Portability.
• The DataflowRunner submits the pipeline to the Google Cloud Dataflow.
• The FlinkRunner runs the pipeline on an Apache Flink cluster. The code has been donated from dataArtisans/flink-dataflow and is now part of Beam.
• The SparkRunner runs the pipeline on an Apache Spark cluster.
• The JetRunner runs the pipeline on a Hazelcast Jet cluster. The code has been donated from hazelcast/hazelcast-jet and is now part of Beam.
• The Twister2Runner runs the pipeline on a Twister2 cluster. The code has been donated from DSC-SPIDAL/twister2 and is now part of Beam.

Have ideas for new Runners? See the runner-ideas label.

Instructions for building and testing Beam itself are in the contribution guide.

Here are some resources actively maintained by the Beam community to help you get started:

To get involved with Apache Beam:

• Subscribe to or e-mail the user@beam.apache.org list.
• Subscribe to or e-mail the dev@beam.apache.org list.
• Join ASF Slack on #beam channel
• Report an issue.