Data Quality Performance Considerations: Optimize Cost & Scale

Soda's approach to data quality emphasizes the importance of performance optimization to prevent cost escalation and maintain trust in data systems. By providing full configurability through YAML configuration files, engineers can manage data quality checks with precision, allowing for efficient resource use and cost control. Soda recommends executing checks only on relevant data slices, thereby reducing unnecessary data processing and associated costs. The platform also encourages grouping multiple checks into single queries to minimize passes over data, further optimizing costs. Additionally, leveraging compute engine-specific features like query caches ensures faster and more cost-effective data profiling. This configuration-first strategy empowers engineers to balance data quality coverage with cost efficiency, ultimately helping to control data warehouse expenses while scaling quality checks across teams.