You're reading from Building Big Data Pipelines with Apache Beam

Product typeBook

Published inJan 2022

Reading LevelBeginner

PublisherPackt

ISBN-139781800564930

Edition1st Edition

Languages

Python

Tools

Apache Beam

Concepts

Data Processing

Author (1)

Jan Lukavský

Introducing and using cross-language pipelines

Cross-language pipelines are a natural concept that comes with Beam's portability. Every executed PTransform in a pipeline has an associated environment. This environment describes how (DOCKER, EXTERNAL, PROCESS) and what (the Python SDK, Java SDK, Go SDK, and so on) should be executed by the Runner so that the pipeline behaves as intended by the pipeline author. Most of the time, all PTransforms in a single pipeline share the same SDK and the same environment. This doesn't necessarily have to be a rule and – when we view this via the optics of the Runner only, the Runner does not care if it executes a Python transform or a Java transform. The Runner code is already written in an (SDK) language-agnostic way, so it should not make any difference.

The first thing we must understand is how is the portable pipeline is represented. When an SDK builds and starts to execute a pipeline, it first compiles it into a portable...

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Building Big Data Pipelines with Apache Beam

Published in: Jan 2022Publisher: PacktISBN-13: 9781800564930

Author (1)

Jan Lukavský

Jan Lukavský is a freelance big data architect and engineer who is also a committer of Apache Beam. He is a certified Apache Hadoop professional. He is working on open source big data systems combining batch and streaming data pipelines in a unified model, enabling the rise of real-time, data-driven applications.
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You're reading from Building Big Data Pipelines with Apache Beam

Introducing and using cross-language pipelines

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