Data Quality, Profiling & Monitoring

О курсе

Learn how to assess, clean, and monitor data quality across modern data pipelines in this comprehensive course within the Data Engineering Skill Path. You will develop essential skills in evaluating source data systems, parsing and harmonizing diverse formats, identifying quality defects through data profiling, and tracing issues to their root cause using data lineage. Through hands-on practice with IBM’s data wrangling and generative AI tools, along with Google’s ETL quality testing methods, you will gain the ability to validate data structures, verify business rules, and build automated monitoring and alerting systems that ensure reliable, high-integrity data. This course blends expert perspectives from IBM and Google, guiding you from foundational data scrubbing to advanced defect detection, lineage-driven troubleshooting, and ongoing quality monitoring. You will progress through the stages of source assessment, wrangling, profiling, remediation, and automated oversight mirroring the full lifecycle of enterprise data quality management. Perfect for aspiring data engineers, data quality analysts, and professionals responsible for maintaining accuracy and consistency in analytical or operational datasets.

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