Starburst Enterprise Context Layer vs Synmetrix

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.

Synmetrix is an open-source data engineering platform and semantic layer for defining and sharing organizational metrics from a central model. It uses Cube data models to define metrics and dimensions, then applies transformations and aggregations so applications can use consistent definitions. The maker says it connects to more than 50 data sources and can integrate with any BI tool. Users can explore metrics in its interface or distribute the model through a SQL API to BI tools, dashboards, reports and data science consumers. Model versioning and branching track changes, while query logs, caching and pre-aggregations support data workflows. Role-based access controls govern access to models and metrics; the project also describes upstream data controls for shared security and governance. Synmetrix supports scheduled reports and configurable alerts, with reports deliverable by email, Slack or webhooks. The core is Apache License 2.0 and the platform is intended for self-hosted, including on-premise, deployment. Setup uses Docker and Docker Compose with PostgreSQL. Listed hardware requirements are a 3.2 GHz or faster processor, at least 8 GB RAM and at least 30 GB free disk space.