Apache Beam

About Apache Beam

Advanced unified programming model for defining and executing data processing workflows that can run on any execution engine. Provides portability across multiple execution environments including Apache Flink, Apache Spark, and Google Cloud Dataflow. Ideal for building flexible, scalable data pipelines.

Key Features

• 1Unified batch and streaming programming model with a single API
• 2Portable across runners: Dataflow, Flink, Spark, and Direct runner
• 3Windows and triggers for time-based aggregations in streaming pipelines
• 4Python, Java, and Go SDK support with cross-language transforms
• 5IO connectors for BigQuery, Kafka, Pub/Sub, S3, and databases

How Python Data Engineers Use Apache Beam

Python data engineers use Apache Beam to write portable data pipelines that run locally for testing and deploy to Google Dataflow or Flink in production without code changes. The Python SDK's `PCollection` API chains transforms — `ReadFromBigQuery | Map(transform_fn) | WriteToBigQuery` — enabling the same pipeline logic to handle both batch backfills and live streaming.