Apache Beam
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An Apache Beam is an open-source unified data processing platform, that includes ETL, batch and stream (continuous) processing.
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- Counter-Example(s):
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- See: Big Data System, Pipeline (Computing), Software Development Kit, GCP Beam, GCP Dataflow.
References
2021
- (Wikipedia, 2021) ⇒ https://en.wikipedia.org/wiki/apache_Beam Retrieved:2021-11-26.
- Apache Beam is an open source unified programming model to define and execute data processing pipelines, including ETL, batch and stream (continuous) processing. 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 Samza, Apache Spark, and Google Cloud Dataflow.
2017
- https://beam.apache.org/
- QUOTE: Apache Beam provides an advanced unified programming model, allowing you to implement batch and streaming data processing jobs that can run on any execution engine.
Apache Beam is:- UNIFIED - Use a single programming model for both batch and streaming use cases.
- PORTABLE - Execute pipelines on multiple execution environments, including Apache Apex, Apache Flink, Apache Spark, and Google Cloud Dataflow.
- EXTENSIBLE - Write and share new SDKs, IO connectors, and transformation libraries.
- QUOTE: Apache Beam provides an advanced unified programming model, allowing you to implement batch and streaming data processing jobs that can run on any execution engine.
2017
- https://beam.apache.org/get-started/beam-overview/
- QUOTE: Apache Beam is an open source, unified model for defining both batch and streaming data - parallel processing pipelines. Using one of the open source Beam SDKs, you build a program that defines the pipeline. The pipeline is then executed by one of Beam’s supported distributed processing back-ends, which include Apache Apex, Apache Flink, Apache Spark, and Google Cloud Dataflow.
Beam is particularly useful for Embarrassingly Parallel data processing tasks, in which the problem can be decomposed into many smaller bundles of data that can be processed independently and in parallel. You can also use Beam for Extract, Transform, and Load (ETL) tasks and pure data integration. These tasks are useful for moving data between different storage media and data sources, transforming data into a more desirable format, or loading data onto a new system.
- QUOTE: Apache Beam is an open source, unified model for defining both batch and streaming data - parallel processing pipelines. Using one of the open source Beam SDKs, you build a program that defines the pipeline. The pipeline is then executed by one of Beam’s supported distributed processing back-ends, which include Apache Apex, Apache Flink, Apache Spark, and Google Cloud Dataflow.