Data Sentinel: A Declarative Production-Scale Data Validation Platform

• Main Authors: Swami, Arun, Vasudevan, Sriram, Huyn, Joojay
• Format: Conference Proceeding
• Language: English
• Subjects: Big Data; Business; Data integrity; Debugging; LinkedIn; Production; Schedules
• ISSN: 2375-026X
• DOI: 10.1109/ICDE48307.2020.00140

Many organizations process big data for important business operations and decisions. Hence, data quality greatly affects their success. Data quality problems continue to be widespread, costing US businesses an estimated 600 billion annually. To date, addressing data quality in production environments still poses many challenges: easily defining properties of high-quality data; validating production-scale data in a timely manner; debugging poor quality data; designing data quality solutions to be easy to use, understand, and operate; and designing data quality solutions to easily integrate with other systems. Current data validation solutions do not comprehensively address these challenges. To address data quality in production environments at LinkedIn, we developed Data Sentinel, a declarative production-scale data validation platform. In a simple and well-structured configuration, users declaratively specify the desired data checks. Then, Data Sentinel performs these data checks and writes the results to an easily understandable report. Furthermore, Data Sentinel provides well-defined schemas for the configuration and report. This makes it easy for other systems to interface or integrate with Data Sentinel. To make Data Sentinel even easier to use, understand, and operate in production environments, we provide Data Sentinel Service (DSS), a complementary system to help specify data checks, schedule, deploy, and tune data validation jobs, and understand data checking results. The contributions of this paper include the following: 1) Data Sentinel, a declarative production-scale data validation platform successfully deployed at LinkedIn 2) A generic design to build and deploy similar systems for production environments 3) Experiences and lessons learned that can benefit practitioners with similar objectives.