Data warehouse health is critical for reliable analytics and business intelligence. This article outlines a structured approach to evaluate key dimensions: data quality, query performance, schema design, and operational maintainability. It provides practical metrics and checklists that data teams can adopt to identify bottlenecks, reduce costs, and improve data trust. The framework is vendor-agnostic and applicable to cloud-native warehouses like Snowflake, BigQuery, and Redshift, as well as on-premise solutions. For engineering leaders, this offers a systematic way to communicate warehouse health to stakeholders and prioritize improvements. The post emphasizes continuous monitoring rather than one-time audits, making it a evergreen reference for data governance.
A framework for assessing data warehouse health, covering key metrics and best practices.