Optimize Dataflow for Real-Time and Aggregate Data

  • 12/6/2020
  • One-minute read

Google’s guidance on processing real-time and aggregate data with Dataflow demonstrates how to split pipelines based on freshness requirements.

Key Ideas

  • Use side outputs to route events into low-latency streams versus batch aggregations.
  • Design windowing strategies (fixed, sliding, session) that match business SLAs for each use case.
  • Apply dynamic destinations to write realtime metrics to BigQuery while archiving raw data to Cloud Storage for longer-term processing.

Practical Advice

  • Keep transforms reusable across both paths to avoid divergence in business logic.
  • Monitor watermark progression to ensure backlog doesn’t delay aggregate outputs.
  • Budget resources separately; realtime pipelines may prioritise CPU, while batch jobs benefit from wider parallelism and flex templates.
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