Bring consistency to data lake operations by enabling teams to manage cataloging, schema evolution, and optimization directly through SQL. Define standardized commands for creating tables, evolving schemas, compacting data, and setting retention policies. Extend SQL capabilities with governance primitives such as GRANT/REVOKE for fine-grained access control, CHECK constraints for data quality, and metadata tagging for classification and lineage.

Operational queries should expose key metrics, file counts, partition balance, and small-file ratios, along with commands to auto-optimize structures. Ensure atomicity by wrapping operations in transactions, maintaining both metadata and data integrity.

By using SQL as the control surface, engineers codify best practices while analysts operate confidently within established boundaries. The result is a unified, transparent, and efficient ecosystem where the data lake performs with the reliability of a data warehouse, without sacrificing flexibility.