Multi-Language Pipelines With Apache Beam
Apache Beam’s portability framework lets teams mix languages (Java, Python, Go) within a single pipeline and run it on multiple runners such as Dataflow, Flink, or Spark without rewriting business logic.
Key Components
- Unified Pipeline API: Express transformations once using Beam SDKs; the runner translates them to engine-specific primitives.
- Portable Execution Graph (PEX): Intermediate representation that keeps pipelines engine-agnostic.
- Expansion Services: Allow Python transforms inside Java pipelines (and vice versa) by hosting language-specific code separately.
When to Use
- You have specialised libraries in one language (e.g., Python ML) but core pipelines in another.
- You want to migrate between managed runners without reimplementing logic.
- Multi-team organisations need a contract that hides runtime details while sharing reusable components.
Considerations
- Manage dependency versions carefully across languages to avoid serialization issues.
- Plan for observability: inspect runner-specific metrics and logs even though the pipeline definition is portable.
- Test portable pipelines end-to-end on the target runner; behaviour can diverge when features are in preview.