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Discover top open-source software, updated regularly with real-world adoption signals.
Discover top open-source software, updated regularly with real-world adoption signals.
Unified model for batch and streaming data pipelines
Apache Beam lets developers write portable batch and streaming pipelines using Java, Python, or Go, then run them on engines like Dataflow, Spark, Flink, or locally with DirectRunner.
Apache Beam provides a unified programming model that abstracts data processing as PCollections transformed by PTransforms. This model works for both bounded (batch) and unbounded (streaming) datasets, allowing the same pipeline code to serve multiple use cases.
Developers choose from official SDKs in Java, Python, and Go, then select a runner that matches their execution environment—Google Cloud Dataflow, Apache Spark, Apache Flink, or the local DirectRunner for rapid development and testing. The framework handles the translation between the abstract model and the specifics of each backend, reducing vendor lock‑in and simplifying pipeline maintenance.
Backed by the Apache Software Foundation, Beam benefits from a vibrant community, extensive documentation, and a growing ecosystem of connectors and transforms. Advanced users can implement custom runners or extend existing SDKs to target new languages or specialized execution platforms.
When teams consider Apache Beam, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
Daily ETL from Cloud Storage to BigQuery
Transforms and loads daily CSV files using the DataflowRunner, ensuring reliable batch processing with automatic scaling.
Real‑time clickstream analytics
Ingests unbounded event streams, aggregates metrics, and writes results to a dashboard via the SparkRunner, providing near‑real‑time insights.
Local pipeline development and CI testing
Uses DirectRunner to execute unit tests and integration checks on a developer’s machine, speeding feedback cycles.
Custom runner for proprietary HPC cluster
Implements Beam’s Runner API to execute pipelines on an internal high‑performance cluster, leveraging existing investment while keeping pipeline code portable.
A unified abstraction that represents data as PCollections and processing steps as PTransforms, allowing the same pipeline code to run in batch or streaming mode.
Official SDKs are available for Java, Python, and Go.
Select based on execution environment: DirectRunner for local testing, DataflowRunner for Google Cloud, SparkRunner for Apache Spark clusters, etc.
Yes, the DirectRunner executes pipelines locally, and other runners can target on‑premise clusters such as Spark or Flink.
Apache Beam is released under the Apache License 2.0.
Project at a glance
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