Beam reads your data from a diverse set of supported sources, no matter if it’s on-prem or in the cloud.
Beam executes your business logic for both batch and streaming use cases.
Beam writes the results of your data processing logic to the most popular data sinks in the industry.
Apache Beam Features
A simplified, single programming model for both batch and streaming use cases for every member of your data and application teams.
Apache Beam is extensible, with projects such as TensorFlow Extended and Apache Hop built on top of Apache Beam.
Execute pipelines on multiple execution environments (runners), providing flexibility and avoiding lock-in.
Open, community-based development and support to help evolve your application and meet the needs of your specific use cases.
Write Once, Run Anywhere
Create Multi-language Pipelines
Try Beam Playground
Beam Playground is an interactive environment to try out Beam transforms and examples without having to install Apache Beam in your environment.
You can try the Apache Beam examples at Beam Playground (Beta).
Case Studies Powered by Apache Beam
Apache Beam has future-proofed Credit Karma’s data and ML platform for scalability and efficiency, enabling MLOps with unified pipelines, processing 5-10 TB daily at 5K events per second, and managing 20K+ ML features.
Apache Beam enabled real-time ML streaming feature generation and model execution playing a pivotal role in optimizing Lyft’s Marketplace ML predictions, processing ~4mil events per minute to generate ~100 features.
Apache Beam provides Ricardo, a leading Swiss second hand marketplace, with a scalable and reliable data processing framework that supports fundamental business scenarios and enables real-time and ML data processing.
Apache Hop, an open-source data orchestration platform, uses Apache Beam to “design once, run anywhere” and creates a value-add for Apache Beam users by enabling visual pipeline development and lifecycle management.