Complete guide to building a data quality framework. Learn constraint validation in Scala, YAML checks (DPAT), Spark/Airflow/CI/CD integration, and automated failure reporting. Includes real code and architecture to catch issues early.
Build reusable Scala utilities for data engineering on GCP, covering CDC, Delta maintenance, schema evolution, GCS helpers, affected-partition strategies, and error handling. Includes patterns for safe ops and repeatable pipelines.
Production-tested strategy to cut pipeline failures from 12% to 2% using YAML-based data quality checks (DPAT), CI/CD gates, and strict data contracts. Includes before/after metrics and architecture for self-healing pipelines in prod.