Unity Catalog governs Databricks data and AI assets with one metastore per region. AWS Glue Data Catalog indexes the location, schema, and runtime metrics of your data sources, with Lake Formation enforcing permissions. Atlan has connectors for both catalogs, and its Context Layer serves business definitions to agents over MCP.
Neither catalog ranks above the other. Each is built for the platform it comes from, and fit depends on where your compute and policy already live. Each catalog now documents federation to the other, so the choice need not be exclusive. If you are also weighing other AWS catalog services against Glue, how the Glue Data Catalog compares with AWS’s domain-based catalog covers that side, and the context layer for Databricks covers the other estate.
| Dimension | Unity Catalog (Databricks) | AWS Glue Data Catalog (AWS) |
|---|---|---|
| What it governs | Tables, views, volumes, functions, models, services | Metadata index: location, schema, runtime metrics |
| Namespace | catalog.schema.object | Databases and tables; single-level namespaces on the Iceberg REST endpoint |
| Permission model | Privileges, attribute-based policies, row and column filters | Lake Formation augments IAM at column, row, and cell level |
| Scope | One metastore per region, shared by workspaces | Region-specific Iceberg REST endpoint |
| Iceberg REST path | /api/2.1/unity-catalog/iceberg-rest |
https://glue.<region>.amazonaws.com/iceberg |
| Authentication | Databricks personal access token or OAuth | SigV4 or OAuth 2.0 bearer token |
| Access prerequisite | External data access on the metastore plus EXTERNAL USE SCHEMA |
With OAuth, Lake Formation permissions on every request |
| Federation to the other | Lakehouse Federation reads Glue; read only | Catalog federation reads Unity Catalog; read operations |
| Agent-facing features | MCP servers on Unity Catalog permissions; Genie One MCP is GA; Unity Gateway MCP integration is Beta | Business context and skill assets (Preview per the AWS page) |
What is the difference between Unity Catalog and AWS Glue Data Catalog?
Unity Catalog is a governance catalog for Databricks data and AI assets, and the Glue Data Catalog is the AWS metadata repository that AWS analytics services and Lake Formation integrate with. One organizes securable objects in a catalog.schema.object namespace. The other indexes where your data lives and what shape it has, then lets Lake Formation decide who sees which columns, rows, and cells.
Both now speak Iceberg REST, and each documents federation to the other. According to the Databricks documentation on connecting to external databases and catalogs (last updated Sep 29, 2026), AWS Glue appears among the supported catalog federation sources. According to the AWS Big Data Blog (12 Jan 2026), Glue can federate Unity Catalog in return. The same table can now be reachable from both catalogs, so the working question is which catalog’s permissions apply on each path.
That question is also where how Glue’s catalog differs from a cross-cloud governance layer becomes relevant. A data catalog and a context layer hold different things, and agents need both. A Context Layer above both catalogs gives an agent one place to look up what “revenue” means.
What does Unity Catalog govern?
Unity Catalog governs data and AI assets, including tables, views, volumes, functions, models, and services, in a catalog.schema.object namespace. According to the Databricks Unity Catalog best practices page (last updated Oct 30, 2026), you can have only one metastore per region, and catalogs are the primary unit of data isolation. All workspaces in a region share that metastore.
Grants go to users, service principals, or groups. Access control covers privileges, attribute-based policies, row and column filters, and workspace bindings, per the Databricks overview (last updated Sep 11, 2026). Model services and MCP services are governed objects too, and the overview lists discovery, lineage, and auditing among Unity Catalog’s capabilities.
The Databricks overview also notes that Unity Catalog is available as an open-source implementation; everything here describes the version built into Databricks. Features built on top of it vary in status, which is why the Unity Catalog business glossary and domains, with GA or Preview status is worth checking before you plan around one. The Unity Catalog semantic layer and Unity Catalog metrics cover the metric definitions that sit beside it.
Because model services and MCP services share the namespace, agent-facing objects are governed through the same grants as tables.
What does AWS Glue Data Catalog govern?
AWS Glue Data Catalog is a centralized repository that stores metadata about your organization’s data sets and indexes the location, schema, and runtime metrics of your data sources. According to the AWS Glue User Guide (undated), Glue integrates with Lake Formation for fine-grained access control on Data Catalog resources.
Lake Formation adds its own permissions model that augments IAM, enforced at column, row, and cell level. Per the AWS Lake Formation Developer Guide (undated), it also offers tag-based and attribute-based access control, cross-account sharing, and CloudTrail audit logs. Hybrid access mode lets you adopt Lake Formation permissions table by table next to IAM policies.
The Data Catalog integrates with Amazon Athena, Amazon EMR, and Amazon SageMaker AI, and it offers managed compaction for Iceberg tables and Iceberg materialized views. Where AWS’s knowledge graph for AI agents fits beside the catalog is covered in what AWS Context is, and the shift toward an agentic data catalog frames why catalogs now serve agents as well as people.
For AWS-centered teams, the catalog answers where data is and who may read it. What a column means to an agent is the job of AWS’s business context, which is still in Preview, covered below.
Unity Catalog vs AWS Glue Data Catalog: permissions, Iceberg REST, and federation compared
The sharpest differences sit in the permission system, the Iceberg REST endpoint, and the direction of federation. Side by side, using each vendor’s own documentation:
| Dimension | Unity Catalog (Databricks) | AWS Glue Data Catalog (AWS) |
|---|---|---|
| Iceberg REST reads and writes | Managed Iceberg tables read and write; foreign Iceberg tables and Delta tables with Iceberg reads are read only | Supports Iceberg REST API operations, table specs v1 and v2, on S3 and S3 Tables |
| Endpoint auth | Personal access token or OAuth; EXTERNAL USE SCHEMA and external data access on the metastore |
SigV4 or OAuth 2.0; with OAuth, Glue enforces Lake Formation on every request |
| Operating note from the docs | Keep federation permissions in place: a lapse, even a brief one, can drop and recreate a foreign catalog’s schemas | Business context is in Preview, with access managed through IAM during Preview |
This is a scenario, not a measured case. A team runs Spark on Databricks and registers reporting tables in S3 with Glue. A Spark read of a Unity Catalog table answers to Unity Catalog privileges. A query through the Glue Iceberg REST endpoint with an OAuth token answers to Lake Formation. A Databricks query over a federated Glue catalog answers to the Unity Catalog side: Databricks describes catalog federation as running directly against object storage on Databricks compute, with foreign catalog table-level access controls.
The Databricks Iceberg REST page (last updated Oct 27, 2026) names Apache Spark, Apache Flink, and Trino as engines, and the Apache Iceberg v3 changes matter to anyone planning a shared table format. A data lakehouse for AI and a cross-platform semantic layer extend that across engines and metrics.
Can Unity Catalog and AWS Glue Data Catalog work together?
Yes. Each catalog documents federation to the other, and each federation is read oriented. Federation here means one catalog exposing the other’s tables as a foreign catalog. A data catalog can sit on top of existing catalogs in the same spirit, adding context without replacing either.
| Direction | What the docs say | Who holds permissions | Caveat |
|---|---|---|---|
| Databricks reads Glue (Lakehouse Federation, catalog federation) | AWS Glue metastore is a supported source; setup uses a connection, external locations, and a foreign catalog | Unity Catalog side | Read only; permission loss, even briefly, can drop and recreate schemas; Iceberg reads need a catalog-level storage location |
| Glue reads Unity Catalog (catalog federation) | OAuth2 for a Databricks service principal | Lake Formation on federated resources | Read operations; Delta needs UniForm for Iceberg metadata |
Federate Glue into Unity Catalog
Databricks contributes catalog federation, which lists the AWS Glue metastore as a supported source. Glue contributes the tables, which Databricks queries directly in object storage through a foreign catalog, with grants managed on the Unity Catalog side.
Federate Unity Catalog into Glue
AWS contributes catalog federation in the Glue Data Catalog, authenticated with OAuth2 for a Databricks service principal. Databricks contributes the tables, and UniForm generates Iceberg metadata for Delta tables. Lake Formation manages fine-grained permissions on the federated resources.
Which catalog fits your AWS and Databricks estate?
Fit follows where compute and policy already live, using what each vendor documents.
Start with Unity Catalog when your workspaces already share a regional metastore, when you want policies expressed as attribute-based controls, or when MCP permissions should follow Unity Catalog grants. Start with AWS Glue Data Catalog when Lake Formation column, row, and cell control is your policy system, when you want to adopt Lake Formation table by table through hybrid access mode, or when AWS-side Iceberg REST access is the main path.
Run both when federation in either direction already fits your estate, accepting that each path is read oriented. Readiness for agents is a separate axis from fit. An AI-ready data estate depends on definitions an agent can trust, and context layer 101 explains why that layer sits above any one catalog. For a wider view of the field, agent context layer tools compared maps the options.
What does an AI agent need from Unity Catalog and AWS Glue Data Catalog?
Both vendors now ship agent-facing features, and an agent that works across both estates still needs one place for business definitions. On the Databricks side, per the Databricks MCP documentation (last updated Oct 23, 2026), Databricks-provided and registered MCP servers use Unity Catalog permissions, with Unity Gateway handling access, credentials, and usage monitoring. Per the Databricks-provided MCPs page (last updated Oct 23, 2026), the Genie One MCP is generally available (Databricks release notes, September 2026). The Databricks SQL MCP runs through Unity Gateway and needs an account admin to enable the Unity Gateway beta; the August 2026 release notes label Databricks-managed MCP connectors in Unity Gateway as Beta, even though Unity Gateway itself is now generally available.
On the AWS side, business context and semantic search is in Preview in US East (N. Virginia), US East (Ohio), US West (Oregon), and Europe (Ireland), and AWS marks it “subject to change.” It adds glossary terms, custom metadata fields, skill assets, and a Glue Search API with semantic search, with access managed through IAM and no asset-level access control during Preview. Adding business context, AWS Glue User Guide holds the status line, and Skill assets for AI agents says skill assets point to a URI location with context agents use, such as which teams own which data domains and how teams define key metrics.
An agent that crosses both estates can meet definitions in two places. Atlan documents connectors for both catalogs and an Atlan MCP server that serves governed data definitions and glossaries to agents, with every action respecting the permissions set in Atlan. Why MCP matters for AI agents covers the protocol, and an MCP connected data catalog shows how catalog quality decides agent accuracy.
A semantic layer defines the metrics, a semantic layer for AI agents extends that to runtime. See context engineering too.
What should you plan for when you run both catalogs?
Each vendor documents operating notes for its federation path.
Foreign Iceberg tables are not automatically refreshed through the Unity Catalog Iceberg REST API, so you run REFRESH FOREIGN TABLE (Databricks documentation, last updated Oct 27, 2026). Losing the federation permissions, even briefly, can drop and recreate a foreign catalog’s schemas (Databricks, Oct 13, 2026). The Glue Iceberg REST endpoint supports single-level namespaces, and AWS documents connecting to a multi-level catalog above the namespace so engines can use catalog.namespace.table notation. Glue business context is Preview.
Databricks MCPs follow Unity Catalog permissions, so an agent’s access changes whenever those permissions change. The MCP server for Databricks and Unity Gateway pages cover the setup.
How Atlan approaches Unity Catalog and AWS Glue Data Catalog
Atlan’s Context Layer connects to both catalogs and brings the business context an agent needs into one graph. Table definitions and access rules stay in each platform.
Atlan documents a connector that crawls Unity Catalog metadata and builds lineage for Databricks, and a connector that fetches metadata from the AWS Glue Data Catalog. The Enterprise Data Graph pulls context from both connectors into one graph. Context Agents draft descriptions, metrics, and ontology, human experts certify them, and the Atlan MCP server, a hosted server available on every Atlan tenant, serves them to agents.
The enterprise context layer rests on a context graph, agents consume it through the agent context layer, and implementing an enterprise context layer walks through the build.
Where business definitions live when an agent crosses both catalogs
The two catalogs federate in both directions, and both directions read. That leaves one open question: when an agent spans a Databricks estate and an AWS estate, which system holds what “active customer” means? Each catalog enforces access on its own tables. A definition kept in a layer above both can be read by any agent, whichever catalog it queries.
Why AI agents need an enterprise context layer makes the longer case. Whether you build that layer or buy it is a decision for your team.
FAQs about Unity Catalog vs AWS Glue Data Catalog
1. Is Unity Catalog Databricks only?
Databricks documents Unity Catalog as the governance layer built into Databricks. Its overview page also states that an open-source implementation is available, separate from the built-in version covered here.
2. What is the difference between Unity Catalog and AWS Glue Data Catalog?
Unity Catalog governs Databricks data and AI assets through privileges, policies, and row and column filters. AWS Glue Data Catalog indexes the location, schema, and runtime metrics of data sources, with Lake Formation enforcing permissions on top of IAM.
3. Can AWS Glue Data Catalog read tables from Unity Catalog?
Yes. An AWS Big Data Blog post documents catalog federation from Glue to Unity Catalog using OAuth2 for a Databricks service principal, with Lake Formation managing permissions on the federated resources. It supports read operations, and Delta tables need UniForm to generate Iceberg metadata.
4. Does Unity Catalog support writes to Glue tables through federation?
Not through federation. Databricks lists catalog federation to AWS Glue as read only, and it queries the data directly in object storage. Separately, Databricks documents managed Iceberg tables in Unity Catalog as supporting reads and writes through the Unity Catalog Iceberg REST catalog.
5. Which permission system applies to an Iceberg query on the Glue endpoint?
With OAuth, Lake Formation. AWS states that Glue validates the token and enforces Lake Formation permissions on every request to the Glue Iceberg REST endpoint. The endpoint accepts SigV4 or an OAuth 2.0 bearer token, and Lake Formation hybrid access mode lets tables use IAM policies next to Lake Formation permissions.
6. Can an AI agent use both catalogs through one interface?
Yes, through a layer that reads from both. Atlan documents connectors for Databricks Unity Catalog and AWS Glue, brings context from both into its Enterprise Data Graph, and serves it to agents over the Atlan MCP server, which respects the permissions set in Atlan. Each catalog still enforces its own access rules on its own tables.
Sources
- Data discovery and cataloging in AWS Glue, AWS Glue User Guide (undated)
- Connecting to the Data Catalog using AWS Glue Iceberg REST endpoint, AWS Glue User Guide (undated)
- What is AWS Lake Formation?, AWS Lake Formation Developer Guide (undated)
- Adding business context, AWS Glue User Guide (undated, Preview)
- Skill assets for AI agents, AWS Glue User Guide (undated, Preview)
- Access Databricks Unity Catalog data using catalog federation in the AWS Glue Data Catalog, AWS Big Data Blog (12 Jan 2026)
- What is Unity Catalog?, Databricks documentation (last updated Sep 11, 2026)
- Unity Catalog best practices, Databricks documentation (last updated Oct 30, 2026)
- Access Databricks tables from Apache Iceberg clients, Databricks documentation (last updated Oct 27, 2026)
- Connect to external databases and catalogs, Databricks documentation (last updated Sep 29, 2026)
- Enable Hive metastore federation for AWS Glue metastores, Databricks documentation (last updated Oct 13, 2026)
- MCPs, Databricks documentation (last updated Oct 23, 2026)
- Databricks-provided MCPs, Databricks documentation (last updated Oct 23, 2026)
- Databricks release notes (September and August 2026 entries)
- How to set up AWS Glue in Atlan, Atlan documentation
- How to set up Databricks in Atlan, Atlan documentation
- Atlan MCP, Atlan documentation
