Apache Beam is an open-source unified programming model and framework for defining and executing big data processing pipelines. It provides a way to write data processing code that is portable across different execution engines or runtimes, such as Apache Flink, Apache Spark, Google Cloud Dataflow, and more.
Apache Beam’s portability framework allows you to write your data processing logic once and then run it on different execution engines without modifying the code. This eliminates the need to rewrite or refactor the code for each specific execution engine, saving time and effort.
The portability framework achieves this by providing a layer of abstraction between the pipeline code and the underlying execution engine. It defines a set of standard APIs and concepts that can be used to express data transformations and processing operations in a consistent manner across different runtimes.
The key components of Apache Beam’s portability framework include:
Pipeline API: Apache Beam provides a high-level API for defining data processing pipelines. It allows you to express complex data transformations and operations in a declarative manner using a set of operators and transforms.
Runners: Runners are implementations of the Apache Beam API that execute the pipeline code on a specific execution engine or runtime. Different runners are available for different platforms, such as Apache Flink, Apache Spark, Google Cloud Dataflow, and more.
Translation Layer: The translation layer converts the pipeline code written using the Apache Beam API into a format that is understood by the specific execution engine. It maps the high-level operations to the corresponding operations supported by the underlying runtime.
Portable Execution Graph (PEG): The Portable Execution Graph is an intermediate representation of the pipeline that captures the logical and physical structure of the data processing operations. It provides a common representation that can be translated to the specific execution engine’s native representation.
By leveraging the portability framework, developers can write data processing pipelines using Apache Beam’s high-level API and then choose the execution engine that best suits their needs without having to rewrite the pipeline logic. This flexibility and portability make it easier to migrate and scale data processing applications across different environments and platforms.