Data governance combines policies, processes, and controls that keep data managed, protected, documented, and appropriately used. In a lakehouse, it connects user trust, compliance, metadata, access, lineage, and operational design. section 4.1.2 section 4.3.3
Build governance around explicit organizational and platform requirements:
- Ask analysts and data scientists about access speed, data quality, freshness, documentation, and trust. section 4.1.2
- Define nonnegotiable controls for access, lineage, encryption, auditing, data residency, and regulatory compliance. section 4.1.2 section 7.5.3
- Select catalogs and interfaces that support the required engines, metadata domains, federation model, extensibility, and interoperability. section 7.5.3 section 4.3.3
- Use branching and tagging to isolate development, preserve known-good versions, support reproducibility, and manage retention. section 11.2.2
Federated governance across regions
A multinational healthcare enterprise needs consistent metadata across regions, cloud providers, on-premises workloads, Spark, Trino, and Flink. Apache Gravitino is selected for federated metadata, unified access, extensible governance, and REST Catalog support for Iceberg tables. section 7.5.3
SELECT ref_name, type, snapshot_id, max_reference_age_in_ms FROM "db "."critical_fact_table.refs ";
Inspect table branches and tags to identify stable, developmental, and release references used for isolation, reproducibility, and retention. section 11.2.2
