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DirectoryData & AnalyticsStreaming & Real-Time AnalyticsBeam

Beam

About Beam

Apache Beam is an open-source, unified programming model for batch and streaming data processing pipelines. Beam reads data from a diverse set of supported sources, whether on-prem or in the cloud. Beam executes business logic for both batch and streaming use cases. Beam writes the results of data processing logic to popular data sinks. Pipelines can be executed on multiple execution environments called runners. Apache Beam is a top-level project at Apache.

Products

  • Beam Playground: An interactive environment to try out Beam transforms and examples without installing Apache Beam.

Used for

  • Reading data from on-prem or cloud sources
  • Processing data for batch and streaming use cases
  • Writing processed data results to data sinks
  • Creating multi-language pipelines
  • Building pipelines that run across multiple execution engines

Who it is for

  • Intended for every member of a data and application team.

From Beam's own website, read 2026-10-23. How we build listings.

How to evaluate Streaming & Real-Time Analytics

This is how the CIOPages Research Team evaluates this category. It is not an assessment of Beam. It comes from our Streaming Data & Event Processing buyer guide.

25%
Throughput, Latency & Durability
Sustained throughput and tail latency at your partition count, ordering and exactly-once / at-least-once delivery guarantees, replication and multi-AZ durability, retention limits, and behavior under broker failure and rebalancing
20%
Operating Model & Architecture
Self-managed vs. fully managed vs. serverless vs. bring-your-own-cloud, KRaft (post-ZooKeeper) operations, diskless / object-storage-backed options, autoscaling, partition rebalancing, and upgrade and patching burden on your team
20%
Kafka-API Compatibility & Portability
Fidelity of the Kafka protocol (consumer groups, transactions, compaction), reuse of existing clients, connectors and tooling, lock-in to a single cloud, and the realistic effort to migrate in or out
15%
Stream Processing & Ecosystem
Native or integrated processing (Flink, Kafka Streams, Spark Structured Streaming, ksqlDB), connector catalog (CDC, sinks, sources), schema registry and governance, and direct landing into Iceberg / Delta lakehouse tables
10%
Security, Governance & Compliance
Encryption in transit and at rest, mTLS / SASL / OAuth and RBAC, audit logging, private networking, data residency and BYOC data-boundary control, and SOC 2 / ISO 27001 / GDPR coverage
10%
Cost Model & Economics at Scale
Pricing unit (capacity / throughput / consumption), inter-AZ and egress charges, storage and retention cost, processing billed separately, and how the bill behaves as volume, partitions, and connectors grow

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Quick Facts

beam.apache.org
CategoryData & Analytics
SubcategoryStreaming & Real-Time Analytics
DeploymentOn-premises, Cloud (managed services)

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