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Ingesting Massive Data Loads: Patterns for High-Performance Batch Pipelines

· 6 min read
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AI & Data Engineering Team

Moving data from source systems into your lake or warehouse sounds simple until you're doing it at scale. A pipeline that works fine at 10M rows starts breaking at 1B — queries time out, storage costs spike, and the pipeline window that should take 2 hours starts taking 14.

This post covers the patterns that separate pipelines that scale from pipelines that collapse under their own weight.