The catalog is where an AI agent finds the right table to answer a business question. Without it, the agent guesses. Databricks Unity Catalog is that catalog for the Databricks platform, the metadata layer your agents query first. It is a centralized metadata layer that simplifies data governance, managing data access, security, and lineage across the Databricks platform.
Watch Context Studio DemoUnity Catalog enhances data discovery by unifying governance for tables, files, and machine learning models. It offers fine-grained access controls to ensure compliance and secure collaboration.
With audit logs and real-time lineage, it provides transparency and accountability for data usage. This governance framework helps enterprises simplify operations and meet regulatory requirements.
Unity Catalog supports multi-cloud environments, making it a versatile solution for modern data management needs.
Unity Catalog does exactly that. It brings user management and metastore for different Databricks workspaces while allowing those workspaces to have their own separate compute, as shown in the image below:
Databricks with and without Unity Catalog - Source: Databricks website.
Unity Catalog is hierarchically arranged in a three-level namespace of catalog, schema, and object, held inside a metastore, as shown in the image below:
Three-level namespace for a hierarchical arrangement of objects in the technical catalog - Source: Databricks website.
This three-level namespace departs from the usual native technical catalog that comes with database and data warehousing systems like MySQL, SQL Server, Oracle, etc. This arrangement of securable objects allows you to use Unity Catalog features like access control, data sharing, discovery, lineage, and logging, among other things.
Let’s now look at how to set up a Unity Catalog for your Databricks account.
How to set up Unity Catalog for a Databricks account
Permalink to “How to set up Unity Catalog for a Databricks account”You need to follow the below-mentioned steps to set up Unity Catalog for your Databricks account:
- Enable Unity Catalog : You need to enable Unity catalog for your account if it’s not already enabled, by default.
- Set up Workspace admin : Then you need to create a user in the admins workspace-local group; this user should be able to grant the account admin and metastore admin roles.
- Provision Databricks compute: Unity Catalog workloads need to comply with the access and security requirements. Databricks documents two access modes: Standard, which can be used by multiple users with data isolation among users, and Dedicated, which can be assigned to and used by a single user or group.
- Grant permission to users: Next, you need to grant your users permission to create objects and access them in Unity Catalog catalogs and schemas.
- Create a catalog : Before you can use Unity Catalog, you need to create at least one catalog, as some Databricks workspaces won’t have catalogs created by default. Follow these best practices when creating a new catalog.
Once you have set up Unity Catalog, you can query all the securable objects, such as schemas, tables, views, etc., and use them for discovery, lineage, access control, and data sharing purposes.
While Unity Catalog excels as a technical data catalog and helps you manage your Databricks environment more effectively, it doesn’t give you one context layer over your entire data stack. This is where Atlan steps in, using the information from Unity Catalog to build that missing context layer. Let’s explore how Atlan and Unity Catalog work together.
Also, read → How to structure Databricks Unity Catalog?
Getting the most out of Databricks Unity Catalog with Atlan
Permalink to “Getting the most out of Databricks Unity Catalog with Atlan”The context layer is a centralized place for managing all your data assets across your wider data ecosystem, not just Databricks. While it is a centralized place, the underlying data architecture and ways of working can follow any operating model.
Atlan integrates with Unity Catalog to offer such a context layer for your data with the following features:
Intuitive user experience that allows you easy access to data assets across your data ecosystem
- Organization-wide business glossary enabling a common business language, making it easy for everyone to understand the KPIs, metrics, and overall business goals better
- Domain-driven data product marketplace for self-contained teams, especially in decentralized organizations
- Governance and quality automation with automatic data classification, tag and lineage propagation, and data contract enforcement
- Embedded collaboration with trust-enabling features like verification, certification, and freshness flags.
Many companies using Databricks to power their core data needs have built on Atlan as the metadata foundation for all of their data stack.
General Motors: Building their Insight Factory using Unity Catalog + Atlan
Permalink to “General Motors: Building their Insight Factory using Unity Catalog + Atlan”
Screenshot from keynote at the DataAISummit - Source: Brian Ames - Leading AI/ML from concept to Production, Head of the AI Center and Senior Manager for Transformation and Enablement at General Motors
“We realized that AI & ML needed to be our competitive advantage and we knew that if that’s our vision, we couldn’t function like a traditional automotive company, we needed to become a software company … our data ecosystem is complex, so we needed to build GM’s Insight Factory right … [partnering with Databricks and other leading solutions], Atlan’s governance solution helps us with end-to-end lineage to understand our ecosystem.”
Brian and the team at GM are transforming their industry and already achieving significant business impact with time-to-insight down from 28 days to 3 hours and $330M added to their bottom line.
Unity Catalog + Atlan: How to integrate
Permalink to “Unity Catalog + Atlan: How to integrate”Making Atlan and Unity Catalog work together is quite easy. You can go through the following steps that are detailed on the Atlan + Databricks Connectivity page to complete the set up:
- Set up authentication between Atlan and Databricks depending on your cloud platform.
- Grant the BROWSE privilege to access an object’s metadata, the lineage graph,
information_schema, and the REST API, among other things.
- Grant Atlan the permissions to import tags from Databricks, reverse sync tags back to Databricks, and extract lineage and usage metadata from Databricks.
Once you’ve set up the connection, Atlan can crawl the metadata from Unity Catalog.
Context layer for data and AI-readiness
Permalink to “Context layer for data and AI-readiness”Atlan was named a Leader in the Forrester Wave report for its design, architecture, and cataloging features.
This comparison was based on 24 different aspects of data cataloging, which can broadly be categorized under the following three themes:
- Automatic cataloging of the entire technology, data, and AI ecosystem
- Enabling the data ecosystem AI and automation first
- Prioritizing data democratization and self-service
With the rise of generative AI in recent years, organizations now have the ability to do more with both structured and unstructured data, provided that the data is searchable, discoverable, and trustworthy. Atlan is the context layer for AI, so the definitions your teams agree on are the same ones your agents read.
Curious how? Talk to us!
Also, read → Databricks To Pursue Automated Data Intelligence | What’s New with Databricks Unity Catalog | A Comprehensive Guide to Databricks Lakehouse AI For Data Scientists
Unity Catalog, Unity AI Gateway, and the neutral context layer
Permalink to “Unity Catalog, Unity AI Gateway, and the neutral context layer”Unity Catalog governs the data, models, and volumes that live inside Databricks; a neutral context layer governs what those assets mean across the whole estate, Databricks included. Unity AI Gateway extends that control to runtime, deciding what agents, models, and MCP services are allowed to do. Atlan is the neutral context layer above both. It unifies definitions, owners, lineage, and policy context from Databricks alongside the warehouses, BI tools, and applications the rest of the estate runs on, holds them in the Enterprise Data Graph, and serves them back to Genie and any other agent through an MCP server. The boundary is ownership of the definition: Unity Catalog owns the Databricks-resident asset, a neutral context layer owns the meaning, and that is what keeps context portable and avoids agent-context lock-in when a platform changes. Lakehouse Federation reaches past the Databricks perimeter, read-only.
| What you need governed | Inside Databricks (Unity Catalog, Unity AI Gateway) | Neutral context layer (Atlan) |
|---|---|---|
| Databricks tables, volumes, and models | Native and authoritative | Reads from Unity Catalog, does not replace it |
| Runtime policy on agents, models, and MCP services | Unity AI Gateway enforces it | Supplies the definitions those policies point at |
| Estate coverage beyond Databricks | Lakehouse Federation only, read-only | Full coverage across connected systems |
| Non-Databricks warehouses and BI tools | Out of scope | Connected as first-class sources |
| Agent-facing context for agents built outside Databricks | Databricks-native agents first | Any agent, served over MCP |
| Portability when the platform changes | Definitions stay inside Databricks | Definitions stay yours, no agent-context lock-in |
How organizations make their data AI-ready with Atlan
Permalink to “How organizations make their data AI-ready with Atlan”The Forrester Wave report compared all the major enterprise data catalogs and named Atlan a Leader. The comparison was based on 24 different aspects of cataloging, broadly across the following three criteria:
- Automatic cataloging of the entire technology, data, and AI ecosystem
- Enabling the data ecosystem AI and automation first
- Prioritizing data democratization and self-service
These criteria made Atlan the ideal choice for a major audio content platform, where the data ecosystem was centered around Snowflake. The platform sought a “one-stop shop for governance and discovery,” and Atlan played a crucial role in ensuring their data was “understandable, reliable, high-quality, and discoverable.”
For another organization, Aliaxis, which also uses Snowflake as their core data platform, Atlan served as “a bridge” between various tools and technologies across the data ecosystem. With its organization-wide business glossary, Atlan became the go-to platform for finding, accessing, and using data. It also significantly reduced the time spent by data engineers and analysts on pipeline debugging and troubleshooting.
A key goal of Atlan is to help organizations maximize the use of their data for AI use cases. As generative AI capabilities have advanced in recent years, organizations can now do more with both structured and unstructured data, provided it is discoverable and trustworthy, or in other words, AI-ready.
Tide’s Story of GDPR Compliance: Embedding Privacy into Automated Processes
Permalink to “Tide’s Story of GDPR Compliance: Embedding Privacy into Automated Processes”- Tide, a UK-based digital bank with nearly 500,000 small business customers, sought to improve their compliance with GDPR’s Right to Erasure, commonly known as the “Right to be forgotten”.
- After adopting Atlan, Tide’s data and legal teams collaborated to define personally identifiable information in order to propagate those definitions and tags across their data estate.
- Tide used Atlan Playbooks (rule-based bulk automations) to automatically identify, tag, and secure personal data, turning a 50-day manual process into mere hours of work.
Book your personalized demo today to see how Atlan establishes and scales data governance across your estate.
FAQs about databricks unity catalog
Permalink to “FAQs about databricks unity catalog”1. What is Databricks Unity Catalog?
Permalink to “1. What is Databricks Unity Catalog?”Databricks Unity Catalog is a centralized metadata layer designed to manage data access, security, and lineage within the Databricks platform. It simplifies data governance by unifying data management across workspaces, enabling secure collaboration.
2. How does Unity Catalog enhance data governance?
Permalink to “2. How does Unity Catalog enhance data governance?”Unity Catalog provides fine-grained access controls, centralized governance, and auditing capabilities. This ensures compliance with data regulations while enabling secure data sharing and collaboration within teams.
3. What are the benefits of using Unity Catalog for metadata management?
Permalink to “3. What are the benefits of using Unity Catalog for metadata management?”Unity Catalog centralizes metadata, making it easier to discover, audit, and govern data. This results in improved data quality, better regulatory compliance, and enhanced collaboration across teams.
4. How can I configure access control in Unity Catalog?
Permalink to “4. How can I configure access control in Unity Catalog?”Unity Catalog allows users to set role-based access controls (RBAC) and fine-grained permissions at the table, file, and cluster levels. This ensures only authorized users can access sensitive data.
5. How does Unity Catalog ensure compliance with data regulations?
Permalink to “5. How does Unity Catalog ensure compliance with data regulations?”Unity Catalog supports detailed audit logs, data lineage, and governance policies. These features help organizations comply with regulatory requirements by providing transparency and control over data access and usage.
6. What are the key differences between Unity Catalog and traditional data catalogs?
Permalink to “6. What are the key differences between Unity Catalog and traditional data catalogs?”Unlike traditional data catalogs, Unity Catalog integrates directly with the Databricks platform, offering governance built into the platform, real-time lineage tracking, and built-in security controls for both structured and unstructured data.
7. Does Databricks Unity Catalog create vendor lock-in for AI agents?
Permalink to “7. Does Databricks Unity Catalog create vendor lock-in for AI agents?”Unity Catalog keeps definitions, tags, and lineage inside the Databricks perimeter, so an agent built outside Databricks cannot read them without rework, and that rework is the real agent-context lock-in cost. Delta Sharing, extended into OpenSharing in June 2026, and the Iceberg REST Catalog open up access to the data itself; the context stays where it was authored. Holding definitions in a neutral context layer keeps them portable across platforms.
