TensorFlow Data Validation: Data Analysis and Validation in Continuous ML Pipelines

Summary: TFDV offers scalable data analysis and validation for continuous ML pipelines, elevating data quality as a first-class concern. Integrated with TensorFlow Extended (TFX), it provides production-grade data monitoring, schema validation, and anomaly detection; open-sourced and widely adopted. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 5880
Venue: SIGMOD
Year: 2020
Pagerank: 7.0451276e-05
Overall Rank: 3,491 | 75.72%
DOI: 10.1145/3318464.3384707

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 6 of 6 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
8,092	Saga: A Scalable Framework for Optimizing Data Cleaning Pipelines for Machine Learning Applications	2023	SIGMOD	4.587921e-05
8,915	DQDF: Data-Quality-Aware Dataframes	2022	VLDB	4.427232e-05
10,771	Unlocking the Power of CI/CD for Data Pipelines in Distributed Data Warehouses	2025	VLDB	4.1945683e-05
10,867	T-Assess: An Efficient Data Quality Assessment System Tailored for Trajectory Data	2025	VLDB	4.1945683e-05
11,280	CM-Explorer: Dissecting Data Ingestion Problems	2023	VLDB	4.1945683e-05
11,342	FILA: Online Auditing of Machine Learning Model Accuracy under Finite Labelling Budget	2022	SIGMOD	4.1945683e-05

Outgoing Citations (Sorted by Pagerank)

Showing 2 of 2 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
1,482	Automating Large-Scale Data Quality Verification	2018	VLDB	0.00011725533
1,627	Data Cleaning: Overview and Emerging Challenges	2016	SIGMOD	0.00011086905

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