Databricks Unity Catalog is a shared metastore at the Databricks account level that streamlines management of multiple Databricks workspaces for users.

Immuta’s Databricks Spark integration with Unity Catalog support uses a custom Databricks plugin to enforce Immuta policies on a Databricks cluster with Unity Catalog enabled. This integration allows you to add your tables to the Unity Catalog metastore so that you can use the metastore from any workspace while protecting your data with Immuta policies.

Unity Catalog Object Model

Databricks clusters with Unity Catalog use 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 the configured 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 tables.

• Catalog: A 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: Tables can be managed or external tables.

For details about the Unity Catalog object model, search for Unity Catalog in Databricks documentation.

Policy Enforcement

Immuta’s Databricks Spark integration with Unity Catalog support uses a custom Databricks plugin to enforce Immuta policies on a Databricks cluster with Unity Catalog enabled. For Immuta to see all relevant tables that have a data source mapped to them, Immuta requires a privileged metastore owner’s personal access token (PAT) from Databricks, and that metastore owner must have been granted access to all the relevant data. This token is stored encrypted to provide an Immuta-enabled Databricks cluster access to more data than a specific user on that cluster might otherwise have.

You must use an Immuta-provided cluster policy to start your Databricks cluster, as these cluster policies explicitly set the data security mode to the Custom setting that allows Immuta to enforce policies on top of Unity Catalog and add Unity Catalog support to the cluster. Once your configuration is complete, policy enforcement will be the same as the policy enforcement for the Databricks Spark integration.

For configuration instructions, see the Configure Databricks Spark Integration with Unity Catalog Support guide.

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 now reference the catalog, schema (also called a database), and the table.

External Tables

If a Databricks table is not a Delta table (if it is an ORC, Parquet, or other file format), it must be an external table. This is a Databricks Unity Catalog restriction and is not related to Immuta. See the Databricks documentation for details about creating these objects to allow external locations to be used.

External locations and storage credentials must be configured correctly on Immuta-enabled clusters to allow tables to be created in a non-managed path. Immuta does not control access to storage credentials or external locations, and a user will have the same level of access to these on an Immuta-enabled cluster as they do on a non-Immuta enabled cluster.

Scratch Paths

Scratch paths are locations in storage that users can read and write to without Immuta policies applied. Immuta's support for scratch paths in Unity Catalog is designed to work with external locations.

You must configure external locations for any scratch path and grant those locations to the metastore owner user being used to connect Immuta. Creating a database in a scratch location in an Immuta-enabled cluster with Unity Catalog differs from how it is supported on a non-Immuta cluster with Unity Catalog; on a non-Immuta cluster, a database will not have a location if it is created against a catalog other than the legacy hive_metastore.

Immuta requires the database location to be specified in the create database call on an Immuta-enabled cluster so that Immuta can validate whether the read or write is permitted, as illustrated in the example below:

CREATE DATABASE mydb LOCATION 's3://bucket/path/mydb';

For configuration instructions, see the Configure Scratch Paths guide.

Data Flow

The data flow for Unity Catalog is the same as the data flow for the Databricks Spark integration.

The only change is that Databricks metadata is saved in Unity Catalog at the account level, not the workspace level.

Databricks metastore magic allows you to migrate your data from the Databricks legacy Hive metastore to the Unity Catalog metastore while protecting data and maintaining your current processes in a single Immuta instance.

Databricks metastore magic is for customers who intend to use either

• the Databricks Spark with Unity Catalog support integration or

• the Databricks Unity Catalog integration, but they would like to protect tables in the Hive metastore.

Requirement

Unity Catalog support is enabled in Immuta.

Databricks metastores and Immuta policy enforcement

Databricks has two built-in metastores that contain metadata about your tables, views, and storage credentials:

• Legacy Hive metastore: Created at the workspace level. This metastore contains metadata of the configured tables in that workspace available to query.

• Unity Catalog metastore: Created at the account level and is attached to one or more Databricks workspaces. This metastore contains metadata of the configured 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 tables.

Databricks allows you to use the legacy Hive metastore and the Unity Catalog metastore simultaneously. However, Unity Catalog does not support controls on the Hive metastore, so you must attach a Unity Catalog metastore to your workspace and move existing databases and tables to the attached Unity Catalog metastore to use the governance capabilities of Unity Catalog.

Immuta's Databricks Spark integration and Unity Catalog integration enforce access controls on the Hive and Unity Catalog metastores, respectively. However, because these metastores have two distinct security models, users were discouraged from using both in a single Immuta instance before metastore magic; the Databricks Spark integration and Unity Catalog integration were unaware of each other, so using both concurrently caused undefined behavior.

Databricks metastore magic solution

Metastore magic reconciles the distinct security models of the legacy Hive metastore and the Unity Catalog metastore, allowing you to use multiple metastores (specifically, the Hive metastore or AWS Glue Data Catalog alongside Unity Catalog metastores) within a Databricks workspace and single Immuta instance and keep policies enforced on all your tables as you migrate them. The diagram below shows Immuta enforcing policies on registered tables across workspaces.

In clusters A and D, Immuta enforces policies on data sources in each workspace's Hive metastore and in the Unity Catalog metastore shared by those workspaces. In clusters B, C, and E (which don't have Unity Catalog enabled in Databricks), Immuta enforces policies on data sources in the Hive metastores for each workspace.

Enforce policies as you migrate

With metastore magic, the Databricks Spark integration enforces policies only on data in the Hive metastore, while the Databricks Spark integration with Unity Catalog support or the Unity Catalog integration enforces policies on tables in the Unity Catalog metastore. The table below illustrates this policy enforcement.

Essentially, you have two options to enforce policies on all your tables as you migrate after you have enabled Unity Catalog in Immuta:

• Enforce plugin-based policies on all tables: Enable the Databricks Spark integration with Unity Catalog support. For details about plugin-based policies, see this overview guide.

• Enforce plugin-based policies on Hive metastore tables and Unity Catalog native controls on Unity Catalog metastore tables: Enable the Databricks Spark integration and the Databricks Unity Catalog integration. Some Immuta policies are not supported in the Databricks Unity Catalog integration. Reach out to your Immuta representative for documentation of these limitations.

Enforcing policies on Databricks SQL

Databricks SQL cannot run the Databricks Spark plugin to protect tables, so Hive metastore data sources will not be policy enforced in Databricks SQL.

To enforce policies on data sources in Databricks SQL, use Hive metastore table access controls to manually lock down Hive metastore data sources and the Databricks Unity Catalog integration to protect tables in the Unity Catalog metastore. Table access control is enabled by default on SQL warehouses, and any Databricks cluster without the Immuta plugin must have table access control enabled.

Supported Databricks cluster configurations

The table below outlines the integrations supported for various Databricks cluster configurations. For example, the only integration available to enforce policies on a cluster configured to run on Databricks Runtime 9.1 is the Databricks Spark integration.

Legend:

Prerequisites

• Databricks Runtime 11.3.

• Unity Catalog enabled on your Databricks cluster.

• Unity Catalog metastore created and attached to a Databricks workspace.

• The metastore owner you are using to manage permissions has been granted access to all catalogs, schemas, and tables that will be protected by Immuta. Data protected by Immuta should only be granted to privileged users in Unity Catalog so that the only view of that data is through an Immuta-enabled cluster.

• You have generated a personal access token for the metastore owner that Immuta can use to read data in Unity Catalog.

• You do not plan to use non-Unity Catalog enabled clusters with Immuta data sources. Once enabled, all access to data source tables must be on Databricks clusters with Unity Catalog enabled on runtime 11.3.

Feature Availability

For details about the supported features listed in the table above, see the pre-configuration details page for Databricks.

Supported Databricks Cluster Configurations

The table below outlines the integrations supported for various Databricks cluster configurations. For example, the only integration available to enforce policies on a cluster configured to run on Databricks Runtime 9.1 is the Databricks Spark integration.

Legend:

Databricks Metastore Magic

Databricks metastore magic allows you to migrate your data from the Databricks legacy Hive metastore to the Unity Catalog metastore while protecting data and maintaining your current processes in a single Immuta instance.

No configuration is necessary to enable this feature. For more details, see the Databricks metastore magic overview.

Caveats and Limitations

• Native workspaces are not supported. Creating a native workspace on a Unity Catalog enabled host is undefined behavior and may cause data loss or crashes.

• Tables must be GRANTed access to the Databricks metastore owner token configured for the integration. For the table to be accessible to the user, the full chain of catalog, schema, and table must all have the appropriate grants to this administrator user to allow them to SELECT from the table.

• Direct file access to Immuta data sources is not supported.

• Limited Enforcement (called available until protected by policy on the App Settings page), which makes Immuta clusters available to all Immuta users until protected by a policy, is not supported. You must set IMMUTA_SPARK_DATABRICKS_ALLOW_NON_IMMUTA_READS and IMMUTA_SPARK_DATABRICKS_ALLOW_NON_IMMUTA_WRITES to false in your cluster policies manually or by selecting Protected until made available by policy in the Databricks integration section of the App Settings page.

• R notebooks may have path-related errors accessing tables.

• Databricks on Azure will return errors when creating a database in a scratch location when Unity Catalog is enabled.

• Databricks accounts deployed on Google Cloud Platform are not supported.

Next

Configure Databricks Spark integration with Unity Catalog support.