Databricks Unity Catalog Integration Reference Guide

Immuta’s integration with Unity Catalog allows you to enforce fine-grained access controls on Unity Catalog securable objects with Immuta policies. Instead of manually creating UDFs or granting access to each table in Databricks, you can author your policies in Immuta and have Immuta manage and orchestrate Unity Catalog access-control policies on your data in Databricks clusters or SQL warehouses:

• Subscription policies: Immuta subscription policies automatically grant and revoke access to specific Databricks securable objects.

• Data policies: Immuta data policies enforce row- and column-level security.

Unity Catalog object model

Unity Catalog uses the following hierarchy of data objects:

• Metastore: Created at the account level and is attached to one or more Databricks workspaces. The metastore contains metadata of all the catalogs, schemas, and tables available to query. All clusters on that workspace use the configured metastore and all workspaces that are configured to use a single metastore share those objects.

• Catalog: Sits on top of schemas (also called databases) and tables to manage permissions across a set of schemas

• Schema: Organizes tables and views

• Table-etc: Table (managed or external tables), view, volume, model, and function

For details about the Unity Catalog object model, see the Databricks Unity Catalog documentation.

Feature support

The Databricks Unity Catalog integration supports

• managing and accessing data across multiple Databricks workspaces

• enforcing Unity Catalog row-, column-, and table-level access controls on Databricks clusters and SQL warehouses:

  • applying column masks and row filters on specific securable objects

  • applying subscription polices on tables and views

• enforcing Unity Catalog access controls, even if Immuta becomes disconnected

• auditing activity of both Immuta users and non-Immuta users

• allowing non-Immuta reads and writes

• using Photon

• using a proxy server

What does Immuta do in my Databricks environment?

Unity Catalog supports managing permissions account-wide in Databricks through controls applied directly to objects in the metastore. To establish a connection with Databricks and apply controls to securable objects within the metastore, Immuta requires a service principal with privileges to manage all data protected by Immuta. Databricks OAuth for service principals (OAuth M2M) or a personal access token (PAT) can be provided for Immuta to authenticate as the service principal. See the Databricks Unity Catalog privileges section for a list of specific Databricks privileges.

Immuta uses this service principal to run queries that set up user-defined functions (UDFs) and other data necessary for policy enforcement. Upon enabling the integration, Immuta will create a catalog that contains these schemas:

• immuta_system: Contains internal Immuta data.

• immuta_policies_n: Contains policy UDFs.

When policies require changes to be pushed to Unity Catalog, Immuta updates the internal tables in the immuta_system schema with the updated policy information. If necessary, new UDFs are pushed to replace any out-of-date policies in the immuta_policies_n schemas and any row filters or column masks are updated to point at the new policies. Many of these operations require compute on the configured Databricks cluster or SQL warehouse, so compute must be available for these policies to succeed.

Workspace-catalog binding

Workspace-catalog binding allows users to leverage Databricks’ catalog isolation mode to limit catalog access to specific Databricks workspaces. The default isolation mode is OPEN, meaning all workspaces can access the catalog (with the exception of the automatically-created workspace catalog), provided they are in the metastore attached to the catalog. Setting this mode to ISOLATED allows the catalog owner to specify a workspace-catalog binding, which means the owner can dictate which workspaces are authorized to access the catalog. This prevents other workspaces from accessing the specified catalogs. To bind a catalog to a specific workspace in Databricks Unity Catalog, see the Databricks documentation.

Use cases

Typical use cases for binding a catalog to specific workspaces include

1. Ensuring users can only access production data from a production workspace environment.

   For example, you may have production data in a prod_catalog, as well as a production workspace you are introducing to your organization. Binding the prod_catalog to the prod_workspace ensures that workspace admins and users can only access prod_catalog from the prod_workspace environment.

2. Ensuring users can only process sensitive data from a specific workspace. Limiting the environments from which users can access sensitive data helps better secure your organization’s data. Limiting access to one workspace also simplifies any monitoring, auditing, and understanding of which users are accessing specific data. This would entail a similar setup as the example above.

3. Giving users read-only access to production data from a developer workspace.

   This enables your organization to effectively conduct development and testing, while minimizing risk to production data. All user access to this catalog from this workspace can be specified as read-only, ensuring developers can access the data they need for testing without risk of any unwanted updates.

Additional workspace connections

Immuta’s Databricks Unity Catalog integration allows users to configure additional workspace connections to support using Databricks' workspace-catalog binding feature. Users can configure additional workspace connections in their Immuta integrations to be consistent with the workspace-catalog bindings that are set up in Databricks. Immuta will use each additional workspace connection to govern the catalog(s) that workspace is bound to in Databricks. If desired, each set of bound catalogs can also be configured to run on its own compute.

To use this feature, you should first set up a workspace-catalog binding in your Databricks account. Once that is configured, you can use Immuta's Integrations API to configure an additional workspace connection. This can be added when you initially set up the integration or by updating your existing integration configuration.

Limitations

• Additional workspace connections in Databricks Unity Catalog are not currently supported in Immuta's connections.
• Each additional workspace connection must be in the same metastore as the primary workspace used to set up the integration.

• No two additional workspace connections can be responsible for the same catalog.

Databricks Unity Catalog privileges

The privileges the Databricks Unity Catalog integration requires align to the least privilege security principle. The table below describes each privilege required in Databricks Unity Catalog for the and the .

Databricks Unity Catalog privilege	User requiring the privilege	Explanation
Account admin	Setup user	This privilege allows the setup user to grant the Immuta service principal the necessary permissions to orchestrate Unity Catalog access controls and maintain state between Immuta and Databricks Unity Catalog.
CREATE CATALOG on the Unity Catalog metastore	Setup user	This privilege allows the setup user to create an Immuta-owned catalog and tables.
Metastore admin	Setup user	This privilege is required only if enabling query audit, which requires granting access to system tables to the Immuta service principal. To grant access, a user that is both a metastore admin and an account admin must grant USE and SELECT permissions on the system schemas to the service principal. See Manage privileges in Unity Catalog for more details.
USE CATALOG and MANAGE on all catalogs containing securables registered as Immuta data sources USE SCHEMA on all schemas containing securables registered as Immuta data sources	Immuta service principal	These privileges allow the service principal to apply row filters and column masks on the securable.
MODIFY and SELECT on all securables registered as Immuta data sources	Immuta service principal	These privileges allow the service principal to apply row filters and column masks on the securable.
OWNER on the Immuta catalog	Immuta service principal	The Immuta service principal must own the catalog Immuta creates during setup that stores the Immuta policy information. The Immuta setup script grants ownership of this catalog to the Immuta service principal when you configure the integration.
USE CATALOG on the system catalog USE SCHEMA on the system.access schema SELECT on the following system tables: system.access.audit system.access.table_lineage system.access.column_lineage	Immuta service principal	These privileges allow Immuta to audit user queries in Databricks Unity Catalog.

Policy enforcement

Immuta’s Unity Catalog integration applies Databricks table-, row-, and column-level security controls that are enforced natively within Databricks. Immuta's management of these Databricks security controls is automated and ensures that they synchronize with Immuta policy or user entitlement changes.

• Table-level security: Immuta manages REVOKE and GRANT privileges on securable objects in Databricks through subscription policies. When you create a subscription policy in Immuta, Immuta uses the Unity Catalog API to issue GRANTS or REVOKES against the catalog, schema, or table in Databricks for every user affected by that subscription policy.
• Row-level security: Immuta applies SQL UDFs to restrict access to rows for querying users.

• Column-level security: Immuta applies column-mask SQL UDFs to tables for querying users. These column-mask UDFs run for any column that requires masking.

The Unity Catalog integration supports the following policy types:

• Subscription policies
• Select masking policies

  • Conditional masking

  • Constant

  • Custom masking

  • Hashing

  • Null (including on ARRAY, MAP, and STRUCT type columns)

  • Regex: You must use the global regex flag (g) when creating a regex masking policy in this integration. You cannot use the case insensitive regex flag (i) when creating a regex masking policy in this integration. See the limitations section for examples.
  • Rounding (date and numeric rounding)

• Row-level policies

  • Matching (only show rows where)

    • Custom WHERE

    • Never

    • Where user

    • Where value in column

  • Minimization

  • Time-based restrictions

Project-scoped purpose exceptions for Databricks Unity Catalog

Project-scoped purpose exceptions for Databricks Unity Catalog integrations allow you to apply purpose-based policies to Databricks data sources in a project. As a result, users can only access that data when they are working within that specific project.

Databricks Unity Catalog views

If you are using views in Databricks Unity Catalog, one of the following must be true for project-scoped purpose exceptions to apply to the views in Databricks:

• The view and underlying table are registered as Immuta data sources and added to a project: If a view and its underlying table are both added as Immuta data sources, both of these assets must be added to the project for the project-scoped purpose exception to apply. If a view and underlying table are both added as data sources but the table is not added to an Immuta project, the purpose exception will not apply to the view because Databricks does not support fine-grained access controls on views.

• Only the underlying table is registered as an Immuta data source and added to a project: If only the underlying table is registered as an Immuta data source but the view is not registered, the purpose exception will apply to both the table and corresponding view in Databricks. Views are the only Databricks object that will have Immuta policies applied to them even if they're not registered as Immuta data sources (as long as their underlying tables are registered).

Masked joins for Databricks Unity Catalog

This feature allows masked columns to be joined across data sources that belong to the same project. When data sources do not belong to a project, Immuta uses a unique salt per data source for hashing to prevent masked values from being joined. (See the Why use masked joins? guide for an explanation of that behavior.) However, once you add Databricks Unity Catalog data sources to a project and enable masked joins, Immuta uses a consistent salt across all the data sources in that project to allow the join.

For more information about masked joins and enabling them for your project, see the Masked joins section of documentation.

Policy exemption groups

Some users may need to be exempt from masking and row-level policy enforcement. When you add user accounts to the configured exemption group in Databricks, Immuta will not enforce policies for those users. Exemption groups are created when the Unity Catalog integration is configured, and no policies will apply to these users' queries, despite any policies enforced on the tables they query.

The principal used to register data sources in Immuta will be automatically added to this exemption group for that Databricks table. Consequently, users added to this list and used to register data sources in Immuta should be limited to service accounts.

Policy support with hive_metastore

When enabling Unity Catalog support in Immuta, the catalog for all Databricks data sources will be updated to point at the default hive_metastore catalog. Internally, Databricks exposes this catalog as a proxy to the workspace-level Hive metastore that schemas and tables were kept in before Unity Catalog. Since this catalog is not a real Unity Catalog catalog, it does not support any Unity Catalog policies. Therefore, Immuta will ignore any data sources in the hive_metastore in any Databricks Unity Catalog integration, and policies will not be applied to tables there.

However, with Databricks metastore magic you can use hive_metastore and enforce subscription and data policies with the Databricks Spark integration.

Authentication methods

The Databricks Unity Catalog integration supports the following authentication methods to configure the integration and create data sources:

• Personal access token (PAT): This is the access token for the Immuta service principal. This service principal must have the metastore privileges listed in the permissions section for the metastore associated with the Databricks workspace. If this token is configured to expire, update this field regularly for the integration to continue to function.
• OAuth machine-to-machine (M2M): Immuta uses the Client Credentials Flow to integrate with Databricks OAuth machine-to-machine authentication, which allows Immuta to authenticate with Databricks using a client secret. Once Databricks verifies the Immuta service principal’s identity using the client secret, Immuta is granted a temporary OAuth token to perform token-based authentication in subsequent requests. When that token expires (after one hour), Immuta requests a new temporary token. See the Databricks OAuth machine-to-machine (M2M) authentication page for more details.

Integration health status

The status of the integration is visible on the integrations tab of the Immuta application settings page. If errors occur in the integration, a banner will appear in the Immuta UI with guidance for remediating the error.

The definitions for each status and the state of configured data platform integrations is available in the response schema of the integrations API. However, the UI consolidates these error statuses and provides detail in the error messages.

Immuta data sources in Unity Catalog

The Unity Catalog data object model introduces a 3-tiered namespace, as outlined above. Consequently, your Databricks tables registered as data sources in Immuta will reference the catalog, schema (also called a database), and table.

Supported object types

The supported object types for Databricks Unity Catalog are listed below. When applying read and write access policies to these data sources, the privileges granted by Immuta vary depending on the object type. See an outline of privileges granted by Immuta on the Subscription policy access types page.

• Table

• View

• Materialized view

• Streaming table

• External table

• Foreign table

External data connectors and query-federated tables

External data connectors and query-federated tables are preview features in Databricks. See the Databricks documentation for details about the support and limitations of these features before registering them as data sources in the Unity Catalog integration.

Query audit

Access requirements

For Databricks Unity Catalog audit to work, Immuta must have, at minimum, the following access.

• USE CATALOG on the system catalog

• USE SCHEMA on the system.access schema

• SELECT on the following system tables:

  • system.access.audit

  • system.access.table_lineage

  • system.access.column_lineage

The Databricks Unity Catalog integration audits user queries run in clusters or SQL warehouses for deployments configured with the Databricks Unity Catalog integration. The audit ingest is set when configuring the integration and the audit logs can be scoped to only ingest specific workspaces if needed.

See the Unity Catalog audit page for details about manually prompting ingest of audit logs and the contents of the logs.

Tag ingestion

Design partner preview: This feature is available to select accounts. Reach out to your Immuta representative to enable this feature.

You can enable tag ingestion to allow Immuta to ingest Databricks Unity Catalog table and column tags so that you can use them in Immuta policies to enforce access controls. When you enable this feature, Immuta uses the credentials and connection information from the Databricks Unity Catalog integration to pull tags from Databricks and apply them to data sources as they are registered in Immuta. If Databricks data sources preexist the Databricks Unity Catalog tag ingestion enablement, those data sources will automatically sync to the catalog and tags will apply. Immuta checks for changes to tags in Databricks and syncs Immuta data sources to those changes every 24 hours.

Once external tags are applied to Databricks data sources, those tags can be used to create subscription and data policies.

To enable Databricks Unity Catalog tag ingestion, see the Configure a Databricks Unity Catalog integration page.

Syncing tag changes

After making changes to tags in Databricks, you can manually sync the catalog so that the changes immediately apply to the data sources in Immuta. Otherwise, tag changes will automatically sync within 24 hours.

When syncing data sources to Databricks Unity Catalog tags, Immuta pulls the following information:

• Table tags: These tags apply to the table and appear on the data source details tab. Databricks tags' key and value pairs are reflected in Immuta as a hierarchy with each level separated by a . delimiter. For example, the Databricks Unity Catalog tag Location: US would be represented as Location.US in Immuta.

• Column tags: These tags are applied to data source columns and appear on the columns listed in the data dictionary tab. Databricks tags' key and value pairs are reflected in Immuta as a hierarchy with each level separated by a . delimiter. For example, the Databricks Unity Catalog tag Location: US would be represented as Location.US in Immuta.

• Table comments field: This content appears as the data source description on the data source details tab.

• Column comments field: This content appears as dictionary column descriptions on the data dictionary tab.

Limitations

• Only tags that apply to Databricks data sources in Immuta are available to build policies in Immuta. Immuta will not pull tags in from Databricks Unity Catalog unless those tags apply to registered data sources.

• Cost implications: Tag ingestion in Databricks Unity Catalog requires compute resources. Therefore, having many Databricks data sources or frequently manually syncing data sources to Databricks Unity Catalog may incur additional costs.

• Databricks Unity Catalog tag ingestion only supports tenants with fewer than 2,500 data sources registered.

Configuration requirements

See the Enable Unity Catalog guide for a list of requirements.

Unity Catalog caveats

• Row access policies with more than 1023 columns are unsupported. This is an underlying limitation of UDFs in Databricks. Immuta will only create row access policies with the minimum number of referenced columns. This limit will therefore apply to the number of columns referenced in the policy and not the total number in the table.

• If you disable table grants, Immuta revokes the grants. Therefore, if users had access to a table before enabling Immuta, they’ll lose access.

• You must use the global regex flag (g) when creating a regex masking policy in this integration, and you cannot use the case insensitive regex flag (i) when creating a regex masking policy in this integration. See the examples below for guidance:

  • regex with a global flag (supported): /^ssn|social ?security$/g

  • regex without a global flag (unsupported): /^ssn|social ?security$/

  • regex with a case insensitive flag (unsupported): /^ssn|social ?security$/gi

  • regex without a case insensitive flag (supported): /^ssn|social ?security$/g

Azure Databricks Unity Catalog limitation

If a registered data source is owned by a Databricks group at the table level, then the Unity Catalog integration cannot apply data masking policies to that table in Unity Catalog.

Therefore, set all table-level ownership on your Unity Catalog data sources to an individual user or service principal instead of a Databricks group. Catalogs and schemas can still be owned by a Databricks group, as ownership at that level doesn't interfere with the integration.

Feature limitations

The following features are currently unsupported:

• Databricks change data feed support

• Immuta project workspaces

• Multiple IAMs on a single cluster

• Column masking policies on views

• Mixing masking policies on the same column

• Row-redaction policies on views

• R and Scala cluster support

• Scratch paths

• User impersonation

• Policy enforcement on raw Spark reads

• Python UDFs for advanced masking functions

• Direct file-to-SQL reads

• Data policies (except for masking with NULL) on ARRAY, MAP, or STRUCT type columns

• Shallow clones

Known issue

Snippets for Databricks data sources may be empty in the Immuta UI.

Next

Configure the Databricks Unity Catalog integration.