A guide to best practices for data monitoring in engineering, covering strategies like establishing clear metrics, automating monitoring processes, integrating oversight into data pipelines, and conducting regular audits. Highlights tools such as Decube, Monte Carlo, Great Expectations, and Soda for data observability and quality validation. Emphasizes setting SMART goals and KPIs, benchmarking against industry standards, and maintaining compliance with regulations like GDPR, HIPAA, SOC 2, and ISO 27001.
Table of contents
IntroductionUnderstand the Importance of Data Monitoring in EngineeringImplement Effective Data Monitoring StrategiesLeverage Advanced Tools for Enhanced Data MonitoringSet Clear Objectives and Benchmarks for MonitoringConclusionFrequently Asked QuestionsList of SourcesSort: