4 Best Data Discovery Methods for Data Engineers to Enhance Quality
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An overview of four data discovery best practices for data engineers, particularly in financial services and telecommunications. Covers understanding data source types (structured, semi-structured, unstructured), blending manual and automated discovery techniques, enforcing data quality through profiling and validation, and fostering cross-team collaboration. The post integrates promotion of Decube's data catalog platform, highlighting features like automated metadata crawling, ML-powered anomaly detection, semantic discovery, and lineage analysis.
Table of contents
IntroductionUnderstand Data Sources and TypesImplement Effective Data Discovery TechniquesEnsure Data Quality During DiscoveryFoster Collaboration and CommunicationConclusionFrequently Asked QuestionsList of Sources173 Impressions