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Apache Beam

Unified model for batch and streaming data pipelines

Stream Processing Engines

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.

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Overview

Overview

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.

Flexibility and Portability

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.

Community and Extensibility

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.

Highlights

Unified programming model for batch and streaming
Language‑specific SDKs (Java, Python, Go)
Portable runners across multiple execution engines
Local DirectRunner for rapid development and testing

Pros

  • Runs on many backends, avoiding vendor lock‑in
  • Strong community and Apache governance
  • Supports both bounded and unbounded data
  • Rich set of transforms and I/O connectors

Considerations

  • Steeper learning curve for new users
  • Performance depends on chosen runner
  • Limited SDKs compared to some proprietary platforms
  • Complexity when building custom runners

Managed products teams compare with

When teams consider Apache Beam, these hosted platforms usually appear on the same shortlist.

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Aiven for Apache Flink

Fully managed Apache Flink service by Aiven.

Amazon Managed Service for Apache Flink logo

Amazon Managed Service for Apache Flink

Serverless Apache Flink for real-time stream processing on AWS.

Azure Stream Analytics logo

Azure Stream Analytics

Serverless real-time analytics with SQL on streams.

Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.

Fit guide

Great for

  • Data teams needing portable pipelines across cloud and on‑prem
  • Engineers building both batch ETL and real‑time analytics
  • Organizations standardizing on a single codebase for multiple runtimes
  • Developers who want to test pipelines locally before production

Not ideal when

  • Simple scripts that don’t require distributed processing
  • Ultra‑low‑latency use cases demanding specialized runtimes
  • Projects limited to a single language without need for portability
  • Teams without access to supported runners or cloud resources

How teams use it

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.

Tech snapshot

Java65%
Python19%
Go9%
TypeScript3%
Dart2%
Shell1%

Tags

batchpythonsqljavagolangbeamstreamingbig-data

Frequently asked questions

What is the Beam programming model?

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.

Which languages can I write Beam pipelines in?

Official SDKs are available for Java, Python, and Go.

How do I choose a runner for my pipeline?

Select based on execution environment: DirectRunner for local testing, DataflowRunner for Google Cloud, SparkRunner for Apache Spark clusters, etc.

Can I run a Beam pipeline without a cloud provider?

Yes, the DirectRunner executes pipelines locally, and other runners can target on‑premise clusters such as Spark or Flink.

What license governs Apache Beam?

Apache Beam is released under the Apache License 2.0.

Project at a glance

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