Apache Beam

Apache Beam
Developer(s) Apache Software Foundation
Initial release June 15, 2016 (2016-06-15)
Stable release
0.2.0 / August 8, 2016 (2016-08-08)
Development status Active
Written in Java, Python
Operating system Cross-platform
License Apache License 2.0
Website beam.apache.org

Apache Beam is an open source unified programming model to define and execute data processing pipelines, including ETL, batch and stream (continuous) processing.[1] Beam Pipelines are defined using one of the provided SDKs and executed in one of the Beam’s supported runners (distributed processing back-ends) including Apache Flink, Apache Spark, and Google Cloud Dataflow[2]

It has been termed an "uber-API for big data".[3]

History

Apache Beam[2] is one implementation of the Dataflow model paper.[4] The Dataflow model is based on previous work on distributed processing abstractions at Google, in particular on FlumeJava[5] and Millwheel.[6][7]

Google released an open SDK implementation of the Dataflow model in 2014 and an environment to execute Dataflows locally (non-distributed) as well as in the Google Cloud Platform service.

In 2016 Google donated the core SDK as well as the implementation of a local runner, and a set of IOs (data connectors) to access Google Cloud Platform data services to the Apache Software Foundation. Other companies and members of the community have contributed runners for existing distributed execution platforms, as well as new IOs to integrate the Beam Runners with existing Databases, Key-Value stores and Message systems. Additionally new DSLs have been proposed to support specific domain needs on top of the Beam Model.

Timeline

Version Original release date Latest version Release date
Current stable version: 0.2.0 2016-08-08 0.2.0 2016-08-08
Old version, no longer supported: 0.1.0 2016-06-15 0.1.0 2016-06-15
Legend:
Old version
Older version, still supported
Latest version
Latest preview version
Future release

See also

References

  1. ↑ Woodie, Alex (22 April 2016). "Apache Beam's Ambitious Goal: Unify Big Data Development". Datanami. Retrieved 4 August 2016.
  2. 1 2 "Cloud Dataflow - Batch & Stream Data Processing".
  3. ↑ Ian Pointer (April 14, 2016). "Apache Beam wants to be uber-API for big data". InfoWorld.
  4. ↑ Akidau, Tyler; Schmidt, Eric; Whittle, Sam; Bradshaw, Robert; Chambers, Craig; Chernyak, Slava; Fernández-Moctezuma, Rafael J.; Lax, Reuven; McVeety, Sam; Mills, Daniel; Perry, Frances (1 August 2015). "The dataflow model" (PDF). Proceedings of the VLDB Endowment. 8 (12): 1792–1803. doi:10.14778/2824032.2824076. Retrieved 4 August 2016.
  5. ↑ Chambers, Craig; Raniwala, Ashish; Perry, Frances; Adams, Stephen; Henry, Robert R.; Bradshaw, Robert; Weizenbaum, Nathan (1 January 2010). "FlumeJava: Easy, Efficient Data-parallel Pipelines" (PDF). Proceedings of the 31st ACM SIGPLAN Conference on Programming Language Design and Implementation. ACM: 363–375. doi:10.1145/1806596.1806638. Retrieved 4 August 2016.
  6. ↑ Akidau, Tyler; Whittle, Sam; Balikov, Alex; Bekiroğlu, Kaya; Chernyak, Slava; Haberman, Josh; Lax, Reuven; McVeety, Sam; Mills, Daniel; Nordstrom, Paul (27 August 2013). "MillWheel" (PDF). Proceedings of the VLDB Endowment. 6 (11): 1033–1044. doi:10.14778/2536222.2536229. Retrieved 4 August 2016.
  7. ↑ Pointer, Ian. "Apache Beam wants to be uber-API for big data". InfoWorld. Retrieved 4 August 2016.
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