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8 Data Pipeline Patterns Exist. Most Engineers Only Know 3
Most pipelines fail not because of bad code, but because engineers picked the wrong pattern from the start.
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Every week, a data team somewhere rewrites its entire pipeline from scratch. Not because the original code was buggy, not because the infrastructure was wrong, but because they chose a pipeline pattern that didn’t match what their business actually needed.
A startup builds a slick ETL pipeline to process daily sales reports — which works great, until the product team asks for real-time fraud detection. A fintech company builds a streaming pipeline for everything — including the monthly billing run that could have been a simple batch job. Six months of engineering work, thrown away.
The truth is, data pipeline design is not about picking the “best” technology. It’s about matching a pattern to a problem. There are exactly 8 patterns that cover virtually every data engineering scenario in production today. Learn these, and you’ll never have to rewrite a pipeline from scratch again.
