Data Pipeline Patterns for Code Review

Overview

Pipelines fail silently, duplicate records, or stall under backpressure — often in production, at scale, and without clear error messages. Use this guide when reviewing data ingestion, transformation, or delivery code to catch structural hazards before they cause data loss or corruption.

When to use: Reviewing ETL/ELT jobs, stream processors, batch schedulers, orchestration DAGs, message consumers, or any code that moves data between systems at scale.

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