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.
Apache Beam Pipeline Runners
The Beam Pipeline Runners translate the data processing pipeline you define with your Beam program into the API compatible with the distributed processing back-end of your choice. When you run your Beam program, you’ll need to specify an appropriate runner for the back-end where you want to execute your pipeline.
Beam currently supports Runners that work with the following distributed processing back-ends:
- Apache Apex
- Apache Flink
- Apache Gearpump (incubating)
- Apache Spark
- Google Cloud Dataflow
|Published on Java Code Geeks with permission by Furkan Kamaci, partner at our JCG program. See the original article here: Apache Beam
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