data-quality-frameworks

Validate data pipelines with Great Expectations, dbt tests, and data contracts. Production patterns for implementing data quality with Great Expectations, dbt tests, and data contracts to ensure reliable data pipelines. - Covers three complementary frameworks: Great Expectations for statistical and schema validation, dbt tests for transformation layer checks, and data contracts for cross-team data agreements - Includes six core quality dimensions (completeness, uniqueness, validity, accuracy, consistency, timeliness) with ready-to-use expectation patterns and custom test examples - Provides checkpoint automation for CI/CD integration, Slack notifications on failure, and orchestrated validation pipelines across multiple tables - Supports both generic reusable tests and singular business-logic tests, with data contract specifications for SLA, freshness, and PII classification - Implementing data quality checks in pipelines - Setting up Great Expectations validation - Building comprehensive dbt test suites - Establishing data contracts between teams - Monitoring data quality metrics - Automating data validation in CI/CD - Test early - Validate source data before transformations - Test incrementally - Add tests as you find issues - Document expectations - Clear descriptions for each test - Alert on failures - Integrate with monitoring - Version contracts - Track schema changes -...