Data Catalog:
What It Is and How It Works

A data catalog is a searchable inventory of data assets and the metadata that describes them. It helps people discover data, understand what it means, and find information about its origin, ownership, and use. As IBM explains, a catalog organizes assets through metadata so users can identify data relevant to their work.

The inventory can extend beyond database tables. IBM’s catalog overview includes files, documents, reports, dashboards, machine learning models, and relationships between systems. The assets visible in a particular catalog depend on what its integrations collect.

How a data catalog works#

A catalog combines metadata collection with descriptions, search, and ongoing updates. The mechanisms differ by product, but these documented functions explain the basic workflow:

1. Collect technical metadata. Connectors or crawlers read information from data sources. In AWS Glue, crawlers scan sources and extract metadata into the Data Catalog; tables can also be defined manually. The stored information includes data locations, schemas, and properties.
2. Add business context. Definitions and relationships connect technical assets to understandable concepts. Microsoft Purview Unified Catalog uses glossary terms to attach business vocabulary to assets and data products to group related tables, files, and reports around a use case.
3. Make the records searchable. Users search the metadata and inspect matching assets. Purview’s search documentation describes keyword search and filters for asset type, source type, classifications, contacts, and assigned terms. Results are limited by permissions to view assets.
4. Maintain the inventory. Changes in source systems need corresponding metadata updates. AWS recommends running crawlers regularly, updating partitions and schemas, and reviewing schema changes before applying them to avoid disrupting downstream applications.

The catalog’s central record is metadata. AWS describes its Data Catalog as an index to the location, schema, and runtime metrics of data sources. Query engines and storage systems use that information to locate and process the underlying data.

What metadata belongs in a data catalog?#

Metadata is information about data: its description, structure, location, and management conditions. IBM’s metadata management guide distinguishes descriptive information such as titles and summaries, technical properties such as format and storage location, and administrative information such as ownership, permissions, and retention policies.

The following fields connect those categories to the questions involved in evaluating an asset:

These fields describe different aspects of an asset. A schema explains structure; a business term explains meaning; a quality result records the outcome of a check. Evaluate the information relevant to the intended use instead of treating any one field as a complete assessment.

Data catalog vs. data dictionary, glossary, and warehouse#

These tools have related roles, and a catalog can connect their information:

The distinction affects implementation: finding an asset through a catalog, understanding its fields, and querying its records are separate tasks. Product suites can combine capabilities, so inspect the actual collection, storage, query, and governance functions offered.

What data lineage reveals#

Data lineage records where data originates, how it moves, and how it is transformed. Microsoft’s lineage documentation describes source-to-destination views that include transformations and support troubleshooting, root cause analysis, and assessment of downstream effects.

The same documentation distinguishes entity-level lineage, where source and target assets are connected by a process, from column-level lineage, which connects source attributes to derived target attributes. It states that granularity varies with the supported data systems in the classic Purview catalog.

When evaluating lineage, check which sources and destinations are represented, which processing steps are captured, and whether the required detail is at the table or column level. Treat missing relationships as coverage questions to investigate. A connector’s ability to list tables does not, by itself, establish that it captures the transformations between them.

How a catalog supports data quality and governance#

Data quality needs explicit checks#

Quality describes how well a dataset serves a particular purpose. AWS Glue Data Quality evaluates rules against datasets; those rules check specific characteristics such as freshness or integrity. Its quality score is the percentage of evaluated rules that pass.

That definition limits what the score establishes: passing the configured rules does not establish that every possible problem has been checked. Read the rules and their results, and confirm when they were evaluated. Identify which checks address the intended use and which questions remain unanswered.

Cataloging and quality evaluation are separate operations. AWS supports quality tasks on datasets already registered in its Data Catalog. An inventory record therefore needs accompanying evaluation evidence to support a quality assessment.

Ownership, authority, and access answer different questions#

Google Cloud’s CDMC implementation guidance separates ownership, authoritative-source registration, classification, entitlements, quality measurement, and lineage into distinct controls for sensitive cloud data. Its ownership field and authoritative-source register are separate requirements.

The distinction provides a useful review framework: identify who is accountable, whether an authoritative source is designated, what sensitivity applies, and what access conditions govern use. The CDMC guidance concerns a specific cloud control framework; apply the relevant organizational requirements when designing catalog records.

Access also applies to metadata visibility. Purview’s search documentation states that results depend on permissions to view assets. Its Unified Catalog overview separately describes access policies and requests for data products. Check both who can discover an asset and who can obtain permission to use its data.

How to implement and maintain a data catalog#

Microsoft’s getting-started guidance provides a concrete sequence: assign governance roles and owners, register and scan sources, build data products, grant catalog reader permissions, attach business concepts, and configure quality assessment. Its initial setup calls for at least one governance domain and a data product within that scope.

The sequence can guide implementation planning:

1. Define the initial scope and responsibility. Identify the assets to include and the owners responsible for their business context.
2. Collect metadata from supported sources. Register connections and inspect the results of scanning.
3. Curate meaning and use conditions. Add descriptions, business terms, ownership, and access information.
4. Test discovery and access. Check whether users with the intended permissions can locate the assets and follow the access process.
5. Evaluate quality. Configure profiling and rules, run assessments, and review the results.

Keep collection and curation active after setup. AWS’s catalog best practices recommend regular crawls, consistent naming, descriptive table names, schema-change review, permission audits, and monitoring of crawler and job performance.

For ongoing review, check source coverage, metadata update status, ownership, access permissions, and quality results. Investigate missing or outdated records before expanding the catalog’s scope.

How to evaluate a data catalog#

Product names do not establish the depth of a catalog’s coverage. AWS Glue documents a metadata repository integrated with AWS analytics services, while Microsoft Purview documents glossary, data-product, and governance workflows. Evaluate the functions required by the data environment and the people who will use it.

Use a demonstration with actual assets to answer these questions:

• Collection: Which required systems and asset types can be scanned? How are schema changes reflected?
• Meaning: Can definitions and business terms be linked to the relevant assets and fields?
• Discovery: Can users find records through the vocabulary, filters, and permissions they need?
• Lineage: Are the required transformation paths visible at the necessary level of detail?
• Quality: Can users inspect the configured rules, their results, and evaluation timing?
• Governance: Who can view or edit metadata, and how are requests for data access handled?
• Maintenance: Who operates connectors, reviews metadata changes, maintains descriptions, and resolves catalog issues?

Record unsupported sources and missing functions alongside the demonstrated capabilities. Use those findings to decide whether the catalog covers the required discovery and evaluation tasks.

Frequently asked questions#