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Data Pipeline Design Patterns

Common patterns for building reliable pipelines: idempotency, incremental loads, backfills and layered architectures.

Editorial team 1 min read

Well-designed pipelines are reliable, maintainable and easy to recover.

Idempotency

Running a pipeline twice for the same period should produce the same result, not duplicates. Use overwrites of partitions or merge operations rather than blind appends.

Incremental Loading

Process only new or changed data, using timestamps, change data capture or watermarks. Faster and cheaper than full reloads.

Backfills

Design pipelines to reprocess historical periods easily, parameterised by date.

Layered Architecture

  • Raw (bronze): data as received.
  • Cleaned (silver): validated, standardised.
  • Curated (gold): business-ready tables and metrics.

Keeping raw data allows reprocessing when logic changes.

Late-Arriving Data

Plan for records arriving after their period has been processed; reprocess recent windows.

Dependency Management

Orchestrators run steps in order and retry failures.

Observability

Log row counts, durations and data quality results for every run.

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