Cloud Dataflow

Fully managed platform for stream and batch data processing that scales automatically to handle ETL pipelines, real-time analytics, and complex transformations. Built on the Apache Beam SDK, it uses a unified programming model so the same pipeline logic runs for both streaming and batch workloads, with state, time semantics, and a large I/O connector ecosystem.

Trusted by enterprises such as Shopify for real-time ML and ANZ Bank for enterprise data lake pipelines. Dataflow scales to thousands of workers per job and routinely processes petabytes, with horizontal autoscaling for both batch and streaming. Unlike vendor-locked alternatives, Beam pipelines are portable across clouds, on-premises, or edge.

Key capabilities:

• Apache Beam unified model for batch and streaming with state, time, and transform support
• Pre-built templates for Pub/Sub, Kafka, CDC, BigQuery, Splunk, Datadog, and more
• Streaming ML with RunInference, MLTransform for preprocessing, and GPU support for gen AI workloads
• Straggler detection, data sampling, and Dataflow Insights for diagnostics and optimization
• Visual job builder and Vertex AI notebooks for iterative pipeline development

Teams use Dataflow for real-time analytics from clickstreams and sensors, ETL and data integration from MySQL or CDC into BigQuery and data lakes, streaming ML for fraud detection and personalization, and log replication to Splunk or other platforms. Integrates with Pub/Sub, BigQuery, Cloud Storage, Spanner, Bigtable, and Vertex AI.