Unlocking the Power of CI/CD for Data Pipelines in Distributed Data Warehouses

Summary: Production-config-driven CI enabling scalable, isolated in‑production tests in large distributed warehouses, cutting overhead and achieving 94.5% pre-prod issue detection at YouTube. Lineage-aware algebraic impact analysis auto-propagates data-quality checks across pipelines to ensure consistency. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID: 14093
Venue: VLDB
Year: 2025
Pagerank: 4.1945683e-05
Overall Rank: 10,771 | 25.07%
DOI: 10.14778/3750601.3750613

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Rank	Citing Paper	Year	Venue	Pagerank

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Showing 3 of 3 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
1,943	Procella: Unifying serving and analytical data at YouTube	2019	VLDB	0.00010012569
3,491	TensorFlow Data Validation: Data Analysis and Validation in Continuous ML Pipelines	2020	SIGMOD	7.0451276e-05
9,908	Keep Your Distributed Data Warehouse Consistent at a Minimal Cost	2023	SIGMOD	4.2576943e-05

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