Apache Beam
Apache Beam: A portable framework for developers building custom batch and streaming ETL pipelines. Ranked #11 of 26 in ETL Software by our editors (8.3/10); pricing: Open source; best for developers building batch and streaming pipelines.
At a glance
- Editor score8.3 / 10
- PricingOpen source
- Best forDevelopers building batch and streaming pipelines
- Paid fromFree
- DeploymentHybrid
- Founded2016
- Facts checked21 Sep 2026
Where it wins
- Unified model for batch and streaming workloads
- Java, Python, and Go SDKs with SQL support
- Runs across Flink, Spark, Dataflow, and local execution
Where it doesn't
- Requires choosing and operating a pipeline runner
- CDC capabilities vary by connector and runner
- Not a hosted ETL SaaS product with vendor-managed tiers
Our verdict on Apache Beam
Apache Beam is an open-source programming model for building batch and streaming data-processing pipelines. It is aimed at developers and data teams that need ETL, data integration, transformations, windowing, aggregation, and custom I/O connectors across local, on-premises, and managed cloud environments. Teams can define pipelines with Java, Python, Go, SQL, TypeScript, or related SDKs, then execute them through supported runners.
Its ecosystem is a central strength. Beam supports portable execution through Apache Flink, Apache Spark, Google Cloud Dataflow, and the local Direct Runner. Built-in integrations cover Apache Kafka, Google BigQuery, Google Cloud Pub/Sub, Google Cloud Storage, JDBC databases, MySQL, PostgreSQL, Oracle, SQL Server, Apache Iceberg, Delta Lake, and Cloud Bigtable. The model also supports custom source and sink connector development, user-defined SQL functions and aggregates, windowing, and event-time processing. That combination fits teams working across multiple processing engines or cloud environments.
Beam’s breadth comes with an architectural trade-off: it is a framework rather than a hosted ETL service. Teams must select an execution runner and account for differences in connector and runner capabilities. CDC support is available in the ecosystem, including documented CDC writes for BigQuery, but capabilities vary by connector and runner. Developers who need custom code, incremental loading, portability, and both batch and streaming workloads should consider Apache Beam. Teams seeking a fully managed ETL interface with vendor-defined plans should look elsewhere.
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Apache Beam pricing
Apache Beam fact sheet
| Free plan | Not verified |
|---|---|
| Paid from | Free |
| Deployment | Hybrid |
| Source connectors | Not verified |
| Destination connectors | Not verified |
| Transformation mode | Mixed |
| Incremental loading | Yes |
| Change data capture | Yes |
| Custom code transforms | Yes |
| Deployment | Cloud, Self-hosted |
| Platforms | Web, Windows, macOS, Linux |
| Support | Community, Docs |
| Built for | Small business, Mid-market, Enterprise (editorial estimate) |
| Integrations | 15 integrations: Apache Flink, Apache Spark, Google Cloud Dataflow, Apache Kafka, Google BigQuery, Google Cloud Pub/Sub … See all → |
| Pricing | Open source |
| Website | beam.apache.org |
| Facts checked | 21 Sep 2026 |
Apache Beam integrations
Apache Beam lists 15 integrations on its own site.
- Apache Flink
- Apache Spark
- Google Cloud Dataflow
- Apache Kafka
- Google BigQuery
- Google Cloud Pub/Sub
- Google Cloud Storage
- JDBC databases
- MySQL
- PostgreSQL
- Oracle
- SQL Server
- Apache Iceberg
- Delta Lake
- Cloud Bigtable
See all Apache Beam integrations →
Alternatives to Apache Beam
- KeboolaManaged ETL with a free tier, broad connectors, visual orchestration, and hybrid deployment.9.4
- Informatica Data IntegrationA hybrid-ready platform for enterprise ETL, ELT, CDC and orchestration.9.3
- Talend Data FabricA broad, governed platform for complex batch, real-time, and hybrid data environments.9.1
See all Apache Beam alternatives →
Also Ranked In
Apache Beam is ranked in 5 of our directories.
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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
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