Dataflow
Fully managed, serverless stream and batch data processing pipeline service powered by Apache Beam.
Fully managed, serverless stream and batch data processing pipeline service powered by Apache Beam.
What is Dataflow?
Google Cloud Dataflow is a serverless stream and batch processing platform based on the open-source Apache Beam SDK. Dataflow abstracts away infrastructure management by automatically provisioning compute worker pools, dynamically rebalancing processing keys across workers (liquid sharding), and horizontally autoscaling workers in response to throughput surges. It natively provides exactly-once processing semantics for streaming pipelines.
Decision Guide: When to Choose This Service
Use Dataflow for high-throughput ETL/ELT data pipelines, real-time event stream transformations (Pub/Sub to BigQuery), exactly-once stream processing, and windowed aggregations over unbounded datasets.
Do NOT use Dataflow for simple point-to-point data copying where Cloud Storage transfer service or BigQuery Data Transfer Service suffices, or for lightweight step orchestration (use Cloud Composer or Workflows).
Core Architectural Pillars
Write pipeline code once in Python, Java, or Go and execute in either batch or streaming mode.
PCollections represent immutable distributed datasets; PTransforms represent operations (Map, Filter, GroupByKey, CoGroupByKey).
Decouples pipeline state storage and shuffle execution from worker VM nodes for faster scaling and lower compute costs.
Group unbounded streaming data into Fixed, Sliding, or Session windows based on event time rather than processing time.
Watermarks track event-time completeness. Late-arriving events exceeding allowed lateness are routed to dead-letter storage.
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