tech-data-pipeline
Data Pipeline Design
Framework
IRON LAW: Data Quality Checks at Every Stage
A pipeline that moves bad data fast is worse than no pipeline — it
corrupts downstream analytics and decisions. Every pipeline stage
(extract, transform, load) must have data quality checks:
row counts, null checks, schema validation, freshness checks.
"Garbage in, garbage out" is not a warning — it's a guarantee.
ETL vs ELT
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