Databricks SQL Warehouse vs Starburst Enterprise Context Layer

Databricks SQL Warehouse has no write-up on Specifiction yet; its figures are above.

Starburst Enterprise Context Layer turns scattered data into governed, versioned Data Products. Each product combines a dataset with business definitions, ownership, access policies, and lineage, and appears in a shared catalog. Controls include role-based access, column masking, and row-level security. Teams can define products in YAML, commit them to git, test changes before merging, and roll back; breaking changes are reviewed before rollout. Automated CI/CD lineage checks verify upstream freshness before a new version is published. A semantic layer turns raw data into business-ready metrics, dimensions, and relationships for BI tools and AI agents. Products can be consumed through JDBC or REST, and the platform connects to more than 50 sources, including Apache Iceberg, Delta Lake, Hive, Amazon S3, Snowflake, and PostgreSQL. It runs in private cloud, hybrid, or on-premises environments and is air-gap ready. The free plan is free. Pro costs what you agree with the maker, billed /credit; Enterprise costs what you agree with the maker, billed /credit; Mission-Critical costs what you agree with the maker, billed /credit.