Apache Beam

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 by one of the provided SDKs and executed in one of the Beam’s supported runners (distributed processing back-ends) including Apache Apex , 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 of the implementation of the Dataflow model paper. [4] The dataflow model is based on a previous distributed workflow 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) 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 to the delivery of distributed execution platforms, as well as new IOs to integrate the Beam Runners with existing Databases, Key-Value Stores and Message Systems. Additionally, the new DSLs have been proposed to support the specific needs of the Beam Model.

Timeline

Version Original release date Latest version Release date
2.1.0 2017-08-23 2.1.0 2017-08-23
2.0.0 2017-05-17 2.0.0 2017-05-17
0.6.0 2017-03-11 0.6.0 2017-03-11
0.5.0 2017-02-02 0.5.0 2017-02-02
0.4.0 2016-12-29 0.4.0 2016-12-29
0.3.0 2016-10-31 0.3.0 2016-10-31
0.2.0 2016-08-08 0.2.0 2016-08-08
0.1.0 2016-06-15 0.1.0 2016-06-15
Legend:
Old version
Older version, still supported
Latest version
Latest preview version

See also

  • List of Apache Software Foundation projects

References

  1. Jump up^ Woodie, Alex (22 April 2016). “Apache Beam’s Ambitious Goal: Unify Big Data Development” . Datanami . Retrieved 4 August 2016 .
  2. ^ Jump up to:a b “Cloud Dataflow – Batch & Stream Data Processing”.
  3. Jump up^ Ian Pointer (April 14, 2016). “Apache Beam wants to be uber-API for big data” . InfoWorld .
  4. Jump up^ 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. Jump up^ Chambers, Craig; Raniwala, Ashish; Perry, Frances; Adams, Stephen; Henry, Robert R .; Bradshaw, Robert; Weizenbaum, Nathan (January 1, 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 . Archived from the original (PDF) on 23 September 2016 . Retrieved 4 August 2016 .
  6. Jump up^ 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 . Archived from the original (PDF) on 1 February 2016 . Retrieved 4 August 2016 .
  7. Jump up^ Pointer, Ian. “Apache Beam wants to be uber-API for big data” . InfoWorld . Retrieved 4 August 2016 .