Automating large-scale data quality verification

作者：Sebastian Schelter, Dustin Lange, Philipp S. Schmidt, Meltem Celikel, Felix Bießmann, Andreas Grafberger · 发表于：Proceedings of the VLDB Endowment · 年份：2018 · DOI：10.14778/3229863.3229867 · 被引用次数：243 · 研究领域：Data Quality and Management、Advanced Database Systems and Queries、Big Data and Business Intelligence

Modern companies and institutions rely on data to guide every single business process and decision. Missing or incorrect information seriously compromises any decision process downstream. Therefore, a crucial, but tedious task for everyone involved in data processing is to verify the quality of their data. We present a system for automating the verification of data quality at scale, which meets the requirements of production use cases. Our system provides a declarative API, which combines common quality constraints with user-defined validation code, and thereby enables 'unit tests' for data. We efficiently execute the resulting constraint validation workload by translating it to aggregation queries on Apache Spark. Our platform supports the incremental validation of data quality on growing datasets, and leverages machine learning, e.g., for enhancing constraint suggestions, for estimating the 'predictability' of a column, and for detecting anomalies in historic data quality time series. We discuss our design decisions, describe the resulting system architecture, and present an experimental evaluation on various datasets.