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Explore the world of multi-language pipelines and learn how to leverage Apache Beam for cross-language data processing.

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Andres Vervaecke

Andres Vervaecke

Data Engineer
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Updated
16 Sep 2025
Published
15 Dec 2022
Reading time
1 min
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 Explore the world of multi-language pipelines and learn how to leverage Apache Beam for cross-language data processing.
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Explore the world of multi-language pipelines and learn how to leverage Apache Beam for cross-language data processing.
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This blog post introduces the concept of multi-language pipelines using Apache Beam. It explains how to leverage I/O connectors and transforms from different languages in your favorite language without reinventing the wheel. The post provides a step-by-step guide with a working demo of a Python pipeline reading and writing data from/to the Java FireStoreIO connector. It covers the implementation of required interfaces in Java, setting up a local expansion service, and building the Python pipeline. Although multi-language pipelines are still in early development and have limitations, this blog post offers a starting point for creating such pipelines.

The blogpost can be found on our Medium channel by clicking this link.

About the author

Andres Vervaecke

Andres is a Machine Learning Engineer with a background in data engineering and cloud infrastructure. Starting on large-scale GCP projects at ML6, he gained expertise in data pipelines, software engineering, and APIs. Now, he focuses on CX use cases, bringing agentic AI solutions into production that are scalable, reliable, and impactful for end-users.

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