Executive Summary
Metadata management is the work of describing, governing and maintaining what you know about your data. A data catalog is the tool most organizations use to do that work.
A data catalog and metadata management are not two products to choose between. Metadata management is the practice, and the catalog is the tool most teams use to run it. Alation describes a data catalog as a tool or an application, and metadata management as a process or function. Informatica describes metadata management as the ongoing discipline of organizing, governing and maintaining information about your data, such as its lineage, ownership, definition and structure. Informatica describes a data catalog as a centralized inventory of data that includes information that describes the data (metadata) and helps organizations efficiently find and understand these assets.
The standards bodies define the underlying terms. ISO states that in all parts of ISO/IEC 11179, metadata refers to descriptions of data. DAMA International describes Metadata Management as providing context and meaning to data, making it discoverable, usable, and auditable. A metadata management program also needs owners, definitions, review cycles and rules for what gets cataloged. No tool supplies those.
This guide explains the difference in more detail, shows where a catalog gets its metadata, lists the capabilities to test, and compares eight data catalog tools across six approaches. Policy, stewardship and access rules are covered in the data governance platform guide.
Data Catalog vs Metadata Management: What Is the Difference?
The two terms are used as if they were rivals in vendor comparisons. They describe different things: one is a body of practice, the other is software.
Metadata. ISO states that in all parts of ISO/IEC 11179, metadata refers to descriptions of data. In a catalog, that covers technical metadata (schemas, columns, data types), business metadata (definitions, owners, glossary terms), operational metadata (usage, freshness, quality scores) and lineage. AWS describes the AWS Glue Data Catalog as a persistent technical metadata store. A store of that kind holds the first category well and the others only as far as you add them.
Metadata management. DAMA International states that the DMBOK is organized around 11 core knowledge areas, with data governance at the center. DAMA International describes Metadata Management as providing context and meaning to data, making it discoverable, usable, and auditable. Informatica describes metadata management as the ongoing discipline of organizing, governing and maintaining information about your data, such as its lineage, ownership, definition and structure. In practice the discipline covers which metadata you collect, who may change a definition, how often descriptions are reviewed, and what happens when a source changes.
Data catalog. Informatica describes a data catalog as a centralized inventory of data that includes information that describes the data (metadata) and helps organizations efficiently find and understand these assets. Alation describes a data catalog as a tool or an application, and metadata management as a process or function. Alation states that effective metadata management processes use a data catalog application to store metadata for search and discovery, governance, and collaboration.
Passive and active metadata. Older catalogs relied on people to register assets and write descriptions, which fell out of date as pipelines changed. Alation describes active metadata management as modernizing the practice through automation, integrations, and continuous monitoring. When a vendor says “active metadata”, ask which metadata it collects without a person, how often, and what it does when it detects a change.
The practical answer to “data catalog vs metadata management” follows from this. Buy a catalog when you have decided how metadata will be managed: which sources come first, who owns definitions, and who reviews what automation proposes. Buying the catalog first and expecting it to supply the practice is the usual reason catalogs go unused.
Where Does a Data Catalog Get Its Metadata?
A catalog is only as complete as the sources it reads. Most estates now hold several catalogs already: one inside each cloud or data platform, plus BI tools that keep their own models. The question is which catalog reads from which, and which one people and AI agents use.
Platform catalogs collect their own metadata. Databricks describes Unity Catalog as the unified governance layer for data and AI built into Databricks. Google Cloud states that Knowledge Catalog automatically crawls and indexes BigQuery datasets, tables, and views. Microsoft states that Data Map scans assets and multicloud sources to capture metadata, and that Unified Catalog is a searchable catalog of the scanned data where you curate, grant access to, and improve the health of your data.
Catalogs read from each other. Collibra lists Databricks Unity Catalog and Google Knowledge Catalog among the sources it ingests metadata from, and states that both also support outbound synchronization. Google Cloud states that Knowledge Catalog can ingest definitions from external systems, such as Oracle or PostgreSQL, or partner registries, such as Collibra, without writing custom pipelines. Snowflake states that Horizon Catalog is built on Apache Polaris and uses Iceberg REST and Scan APIs to let any engine query governed data in place across Snowflake and external catalogs. The Unity Catalog project states that its open source implementation is under the Apache 2.0 license and is compatible with Apache Hive's metastore API and Apache Iceberg's REST catalog API.
Lineage has an open standard. OpenLineage describes itself as an open platform for collection and analysis of data lineage that tracks metadata about datasets, jobs, and runs. Ask each vendor whether it accepts lineage events in that format, which of your schedulers and engines emit them, and whether lineage is column-level or table-level for each source.
AI agents read the catalog too. Google Cloud states that Knowledge Catalog exposes metadata, schemas, lineage, and business rules directly to AI agents using local or remote MCP servers. Alation states that agents access its catalog through MCP and API with the same governed context that people see. Ask whether an agent sees the same access rules as a person, and whether its queries are logged.
Decide which catalog is the record for business definitions, and which systems only receive copies. Two catalogs that both accept edits to the same glossary will drift apart. The data governance platform guide covers the choice between an independent platform and the catalog inside your data platform.
| Metadata | Where it comes from | What to ask |
|---|---|---|
| Technical | Database and warehouse schemas, table and column definitions, file formats | Which sources are scanned without custom code, and how often |
| Business | Glossary terms, definitions, owners and certifications entered by people | Who may edit a definition, and how a change is reviewed |
| Operational | Query logs, usage, freshness and data quality results | Which usage signals are collected, and from which engines and BI tools |
| Lineage | SQL, ETL jobs, BI models and lineage events from schedulers and engines | Column-level or table-level, and for which of your tools |
| Classification | Scans that tag sensitive columns | Which classifiers ship with the product, and how false positives are corrected |
| Exchange | Other catalogs, through connectors or open catalog APIs | Read only or read and write, and which system wins a conflict |
Which Capabilities Should You Test in a Data Catalog Tool?
Every catalog in this guide demos search well. The differences show up on your own sources, with your own definitions, after the first schema change.
Run the proof of concept on three sources: the warehouse or lakehouse where most queries run, the BI tool most people use, and one source the vendor did not suggest. Load your existing glossary. Then change a column in one pipeline and see what the catalog shows the next day.
Test with three groups: an analyst looking for data, a steward editing a definition, and an engineer tracing the effect of a change. A catalog that only the data team opens has not done its job.
| Capability | What it covers | How to test it |
|---|---|---|
| Connectors | Automated collection from databases, warehouses, lakehouses, BI and ETL tools | Connect your top five sources and list the metadata fields each one fills |
| Search and discovery | Finding assets by name, description, term or natural-language question | Have an analyst find the table behind one report without help |
| Business glossary | Terms, definitions, owners and links to assets | Import your glossary and link 20 terms to assets |
| Lineage | Column-level and table-level lineage across pipelines and BI | Trace one report back to its sources and compare with what engineers know |
| Change detection | Updates when schemas, owners or usage change | Drop a column in test and see what the catalog shows next day |
| Classification | Tagging of sensitive data | Scan a table with known personal data and count missed and wrong tags |
| Catalog exchange | Reading from and writing to platform catalogs | Edit a description in each catalog and see where it lands |
| AI access | MCP servers and APIs for AI agents | Ask an agent a question that needs a restricted table and check the answer and the log |
Vendors in Data Catalog Tools & Metadata Management
Representative vendors include Alation, Atlan, Collibra, Databricks Unity Catalog, Google Cloud Knowledge Catalog (formerly Dataplex Universal Catalog), Informatica Cloud Data Governance & Catalog / CDGC (Salesforce), Microsoft Purview (Unified Catalog) and Snowflake Horizon Catalog. The list is alphabetical, is not a ranking, and is not for sale. How we choose vendors
The six approaches below follow where each catalog starts: in a single platform, in a broader data management suite, or independent of any platform.
A catalog built around search and usage ranks assets by how people use them. Alation states that its platform connects to 120+ sources without moving data. Alation states that platform catalogs govern one estate, while an enterprise runs on many.
A stewardship-workflow catalog ties the inventory to glossary terms, owners and lineage. Collibra states that the aim of its Data Catalog is to create and maintain an inventory of an organization's data assets across its entire digital landscape. Collibra states that its Catalog application integrates and displays metadata from databases, data lakes, warehouses, enterprise applications, ETL tools, and BI solutions. Collibra states that technical lineages show the aggregation, manipulation, and transformation of data through ETL tools, files, and ad-hoc SQL.
A context layer for AI treats the catalog as a metadata graph that agents and people both query. Atlan states that it launched publicly in 2020 as the context layer for data. Atlan states that Flexi Credits cover the work Atlan AI does, such as writing descriptions or answering questions.
A catalog inside a data management suite is sold with integration, quality and master data products. Informatica abbreviates Cloud Data Governance & Catalog as CDGC. Informatica states that Data Catalog is part of the AI-powered Informatica Intelligent Data Management Cloud (IDMC) and works with a range of complementary services. Salesforce announced on November 18, 2025 that it has completed its acquisition of Informatica.
Cloud provider catalogs come with the cloud and govern its services first. Microsoft states that all data in Data Map and Unified Catalog is metadata, not the underlying data itself. Microsoft describes data products as a kit of data assets (tables, files, Microsoft Power BI reports, and more) that provides assets with a use case for ease of discovery and understanding. Google Cloud states that Knowledge Catalog, formerly Dataplex Universal Catalog, lets you automatically discover and index technical structures, business context, data quality metrics, and operational relationships across your distributed data estate.
Catalogs built into a data platform enforce access where the queries run. Databricks states that Unity Catalog is automatically enabled for all Databricks workspaces created after November 8, 2023. Databricks states that Unity Catalog is hosted at LF AI & Data, an umbrella foundation of the Linux Foundation. Snowflake describes Horizon Catalog as the agentic catalog for all your data, whether it is inside or outside of Snowflake. Snowflake states that governance policies execute at the query engine layer, not the application layer.
One knowledge-graph catalog from the previous edition now belongs to a workflow software company. ServiceNow states that it announced its plans to acquire data.world in May 2025 and that the deal closed in July 2025. data.world describes its enterprise data catalog as now by ServiceNow. ServiceNow states that ServiceNow Data Catalog integrates with Snowflake, Databricks, AWS, BigQuery, Oracle, and 100+ platforms through zero-copy architecture.
| Vendor | Approach | Where it fits |
|---|---|---|
| Alation | Catalog built around search and usage | Connects to 120+ sources without moving data; MCP and API access for agents |
| Databricks Unity Catalog | Catalog built into a data platform | On by default in new workspaces; open source version under Apache 2.0 |
| Snowflake Horizon Catalog | Catalog built into a data platform | Built on Apache Polaris; policies enforced in the query engine |
| Informatica Cloud Data Governance & Catalog / CDGC (Salesforce) | Catalog inside a data management suite | Part of IDMC; consumption-based pricing; owned by Salesforce since November 2025 |
| Google Cloud Knowledge Catalog (formerly Dataplex Universal Catalog) | Cloud provider catalog | Indexes BigQuery automatically; ingests partner registries; MCP servers for agents |
| Microsoft Purview (Unified Catalog) | Cloud provider catalog | Data Map scans sources; Unified Catalog curates data products; billed per governed asset |
| Atlan | Context layer for AI | Priced mainly by cataloged assets; AI work billed in Flexi Credits |
| Collibra | Stewardship-workflow catalog | Inventory, technical lineage, and synchronization with Unity Catalog and Knowledge Catalog |
Eight catalogs across six approaches; the two cloud catalogs share one, and so do the two data platform catalogs. The approaches were written before any vendor was chosen, and a row in this table is not for sale. data.world, now part of ServiceNow, is covered in the text above. Any catalog vendor, including open source projects and their commercial sponsors, can present its own product in the Spotlight below.
How Are Data Catalog Tools Priced?
Catalog prices follow what you catalog, who uses it, and how much automated work the vendor runs for you. Few vendors publish list prices.
One cloud vendor publishes a meter. Microsoft states that assets collected in Microsoft Purview Data Map but not linked to governance concepts aren't counted as governed assets. Microsoft gives a pricing example in which an organization with 200 unique governed assets in Unified Catalog pays $100 for a 30-day month, without discounts. Scanning and data quality runs are metered separately on the same page, so model them alongside governed assets.
Independent vendors quote. Atlan states that the number of assets you catalog is the main driver of platform price. Atlan states that your order form sets the prices for your organization. Informatica states that it offers consumption-based pricing, under which you pay only for what you use.
Catalogs built into a data platform are part of the platform bill: you pay for the compute and storage the platform charges for, not for a catalog license. Ask which catalog features need a higher edition or add compute charges.
AI features are increasingly priced on their own. Descriptions written by AI, conversational search and agent access can draw on credits or compute that the base subscription does not include.
| Model | You are charged for | Grows with | Where it goes wrong |
|---|---|---|---|
| Per governed or cataloged asset | Tables, columns, reports or models in the catalog | Assets cataloged | Column-level counting multiplies the total after a broad first scan |
| Per user or seat | Editors, stewards and admins | Headcount | Read-only users counted as paid seats |
| Consumption | Scans, profiling and quality jobs | Scan frequency and data volume | Daily full scans on sources that change monthly |
| AI credits | Generated descriptions, conversational answers, agent calls | AI usage | Credit pools that run out mid-year |
| Platform bundle | The data platform, with the catalog included | Platform compute and storage | Features that need a higher edition |
How long does implementation take for Data Catalog Tools & Metadata Management?
A catalog rollout succeeds when people outside the data team use it. Start with a small set of sources tied to one business question, and add more when the first set is used.
Pick the first three to five sources and the reports that depend on them. Name an owner for each business term, and decide which catalog holds the record for definitions if you run more than one.
Connect the sources, the BI tool and the pipelines that feed them. Check which metadata each connector fills, and set scan schedules to match how often each source changes.
Load the glossary, link terms to assets, and certify the assets behind the chosen reports. Review AI-generated descriptions before they are published.
Put catalog links in the BI tool and in team channels, and track searches by people outside the data team. Add sources in the order people ask for them.
What should you ask vendors about Data Catalog Tools & Metadata Management?
Ask each finalist to answer in writing and to show each answer on your own sources.
-
Does most of your data sit in one cloud or one data platform?Yes Start with that platform’s catalog and list what it does not reach.No Shortlist independent catalogs and test each against your top five platforms.
-
Do you already run named data owners and stewards?Yes Weight glossary, approval workflow and certification.No Weight automated collection and search, and assign owners during the pilot.
-
Will AI agents query the catalog this year?Yes Test MCP and API access with your access rules applied.No Ask for the roadmap, and keep AI credits out of the first contract.