A database stores data. A catalog stores knowledge about data. These are distinct concerns, and conflating them is one of the most common reasons enterprise data estates become unusable at scale. Data Cataloging is the systematic practice of recording the metadata that makes datasets discoverable, interpretable, and trustworthy, for humans and to AI systems alike.
Without a maintained catalog, an organization with a thousand Iceberg tables has effectively the same discoverability as one with ten. The data exists, but no one outside the team that created each table can reliably find or correctly use it.
Two Layers of Catalog
There are two distinct catalog layers in a modern lakehouse, and they serve different purposes.
The technical catalog (Apache Polaris is the leading open implementation) tracks the information that query engines need: where the Iceberg table's metadata files live in object storage, the current schema, the partition spec, and the access control policies. This catalog is what Dremio consults before executing any query. It answers the question "how do I read this table?"
The business catalog (implemented in tools like DataHub, Atlan, or OpenMetadata) tracks the information that people and AI agents need: what the table represents in business terms, what each column means, who owns it, what its quality history looks like, where the data came from, and which downstream dashboards depend on it. It answers the question "should I trust this table, and am I interpreting it correctly?"
These two layers are complementary. The technical catalog is a prerequisite for query execution. The business catalog is a prerequisite for correct AI agent behavior.
What a Production Catalog Entry Contains
A complete catalog entry for a single Iceberg table should include: the physical storage location, the full schema with column-level descriptions written in plain business language, a data owner and data steward contact, a classification tag identifying which columns contain PII or are confidential, a data quality score from the most recent quality check run, the lineage of how this table was produced (which pipeline, which source tables), and which downstream tables or dashboards consume it. Tables missing more than two of these fields are effectively undocumented and should be treated as untrusted by automated systems.
Catalog Quality and AI Agent Accuracy
The accuracy of an AI agent's SQL output is directly proportional to the quality of the catalog metadata it reads before writing a query. An agent that reads a column description saying "revenue net of refunds, in USD, for the month of the transaction date" will write different SQL than one that reads only the column name "net_rev." The first version is likely to be correct. The second is a guess.
This means catalog maintenance is not a documentation chore; it is a prerequisite for trustworthy AI analytics. Organizations planning to deploy AI agents against their lakehouse should treat catalog completeness as a hard readiness gate, not a nice-to-have.
