Building a Metadata-Driven Data Quality Framework
Summary
The article outlines how to build a metadata-driven data quality framework for enterprise data platforms. It shows how teams can store validation rules in a centralized metadata repository and generate checks dynamically at runtime with Python, Snowflake, Databricks, and Great Expectations. It also describes operational benefits such as reduced technical debt, better schema drift handling, and centralized observability. The piece closes with production guardrails like fault isolation, idempotency, GitOps-style metadata changes, and alerting integrations for Slack or PagerDuty.
AskAI Classifications
Sectors
General
Functions
Developer and IT Infrastructure
Bug and Testing management
Workflow Orchestration
