Data Ingestion (Metadata Ingestion)

In a data catalog context, data ingestion — more precisely, metadata ingestion — is the automated process of harvesting metadata from source systems (databases, data warehouses, BI tools, pipelines, messaging platforms, and API specifications) and loading it into the catalog. Connectors read each system's native metadata: table and column schemas, dashboard definitions, pipeline configurations, query logs, and usage statistics. Ingestion typically runs on a schedule so the catalog stays synchronized as sources evolve, and richer workflows add profiling (statistics about the data itself) and lineage extraction (how data flows between assets).

The key point: ingestion reads metadata about the data, not the business data itself — which is why a catalog can document sensitive systems without becoming a copy of them.

For a Saudi DMO, automated ingestion determines whether the governance program scales. The NDMO framework expects a maintained data catalog and metadata management practice across the organization, and no team can document hundreds of systems by hand — manual inventories are outdated the week they are finished. Automated ingestion produces a living inventory: new tables appear in the catalog automatically, classification and stewardship workflows pick them up, and PDPL-relevant personal data can be detected as it arrives rather than discovered in an audit. Connector coverage across the organization's actual stack is therefore one of the first criteria when evaluating catalog platforms.