Use Unity Catalog with your Delta Live Tables pipelines

Preview

Delta Live Tables support for Unity Catalog is in Public Preview.

Databricks recommends configuring Delta Live Tables pipelines with Unity Catalog.

Pipelines configured with Unity Catalog publish all defined materialized views and streaming tables to the specified catalog and schema. Unity Catalog pipelines can read from other Unity Catalog tables and volumes.

To manage permissions on the tables created by a Unity Catalog pipeline, use GRANT and REVOKE.

Requirements

Permissions required to create tables in Unity Catalog from a Delta Live Tables pipeline:

• USE CATALOG privileges on the target catalog.

• CREATE MATERIALIZED VIEW and USE SCHEMA privileges on the target schema if your pipeline creates materialized views.

• CREATE TABLE and USE SCHEMA privileges on the target schema if your pipeline creates streaming tables.

• If a target schema is not specified in the pipeline settings, you must have CREATE MATERIALIZED VIEW or CREATE TABLE privileges on at least one schema in the target catalog.

Compute required to run a Unity Catalog-enabled pipeline:

• Shared access mode clusters. A Unity Catalog-enabled pipeline cannot run on a single user (“assigned”) cluster. See Shared access mode limitations on Unity Catalog.

Compute required to query tables that are created by a Delta Live Tables pipeline using Unity Catalog (including streaming tables and materialized views) includes any of the following:

• SQL warehouses

• Shared access mode clusters on Databricks Runtime 13.3 LTS or above.

• Single user (or “assigned”) access mode clusters on 13.3 LTS and above, only if the table owner runs the query.

Additional compute limitations apply. See the section that follows.

Limitations

The following are limitations when using Unity Catalog with Delta Live Tables:

• By default, only the pipeline owner and workspace admins can view the driver logs from the cluster that runs a Unity Catalog-enabled pipeline. To allow other users to access the driver logs, see Allow non-admin users to view the driver logs from a Unity Catalog-enabled pipeline.

• Existing pipelines that use the Hive metastore cannot be upgraded to use Unity Catalog. To migrate an existing pipeline that writes to Hive metastore, you must create a new pipeline and re-ingest data from the data source(s).

• JARs are not supported. Only third-party Python libraries are supported. See Manage Python dependencies for Delta Live Tables pipelines.

• Data manipulation language (DML) queries that modify the schema of a streaming table are not supported.

• A materialized view created in a Delta Live Tables pipeline cannot be used as a streaming source outside of that pipeline, for example, in another pipeline or a downstream notebook.

• If a pipeline publishes to a schema with a managed storage location, the schema can be changed in a subsequent update, but only if the updated schema uses the same storage location as the previously specified schema.

• Tables are stored in the storage location for the target schema. If a schema storage location is not specified, tables are stored in the catalog storage location. If schema and catalog storage locations are not specified, tables are stored in the root storage location of the metastore.

• The Catalog Explorer History tab does not show history for streaming tables or materialized views.

• The LOCATION property is not supported when defining a table.

• Unity Catalog-enabled pipelines cannot publish to the Hive metastore.

• Python UDFs are not supported.

• You cannot use Delta Sharing with a Delta Live Tables materialized view or streaming table published to Unity Catalog.

• You cannot use the event_log table valued function in a pipeline or query to access the event logs of multiple pipelines.

• You cannot share a view created over the event_log table valued function with other users.

• Single-node clusters are not supported with Unity Catalog-enabled pipelines. Because Delta Live Tables might create a single-node cluster to run smaller pipelines, your pipeline might fail with an error message referencing single-node mode. If this occurs, specify at least one worker when you configure compute. See Configure compute for a Delta Live Tables pipeline.

Note

The underlying files supporting materialized views might include data from upstream tables (including possible personally identifiable information) that do not appear in the materialized view definition. This data is automatically added to the underlying storage to support incremental refreshing of materialized views.

Because the underlying files of a materialized view might risk exposing data from upstream tables not part of the materialized view schema, Databricks recommends not sharing the underlying storage with untrusted downstream consumers.

For example, suppose a materialized view definition includes a COUNT(DISTINCT field_a) clause. Even though the materialized view definition only includes the aggregate COUNT DISTINCT clause, the underlying files will contain a list of the actual values of field_a.

Can I use Hive metastore and Unity Catalog pipelines together?

Your workspace can contain pipelines that use Unity Catalog and the legacy Hive metastore. However, a single pipeline cannot write to the Hive metastore and Unity Catalog. Existing pipelines that write to the Hive metastore cannot be upgraded to use Unity Catalog.

Existing pipelines not using Unity Catalog are unaffected by creating new pipelines configured with Unity Catalog. These pipelines continue to persist data to the Hive metastore using the configured storage location.

Unless specified otherwise in this document, all existing data sources and Delta Live Tables functionality are supported with pipelines that use Unity Catalog. Both the Python and SQL interfaces are supported with pipelines that use Unity Catalog.

Changes to existing functionality

When Delta Live Tables is configured to persist data to Unity Catalog, the lifecycle of the table is managed by the Delta Live Tables pipeline. Because the pipeline manages the table lifecycle and permissions:

• When a table is removed from the Delta Live Tables pipeline definition, the corresponding materialized view or streaming table entry is removed from Unity Catalog on the next pipeline update. The actual data is retained for a period so that it can be recovered if deleted by mistake. The data can be recovered by adding the materialized view or streaming table back into the pipeline definition.

• Deleting the Delta Live Tables pipeline results in deleting all tables defined in that pipeline. Because of this change, the Delta Live Tables UI is updated to prompt you to confirm the deletion of a pipeline.

• Internal backing tables, including those used to support APPLY CHANGES INTO, are not directly accessible by users.

Write tables to Unity Catalog from a Delta Live Tables pipeline

Note

If you do not select a catalog and target schema for a pipeline, tables are not published to Unity Catalog and can only be accessed by queries in the same pipeline.

To write your tables to Unity Catalog, when you create a pipeline, select Unity Catalog under Storage options, select a catalog in the Catalog drop-down menu, and select an existing schema or enter the name for a new schema in the Target schema drop-down menu. To learn about Unity Catalog catalogs, see What are catalogs in Databricks?. To learn about schemas in Unity Catalog,see What are schemas in Databricks?.

Ingest data into a Unity Catalog pipeline

Your pipeline configured to use Unity Catalog can read data from:

• Unity Catalog managed and external tables, views, materialized views and streaming tables.

• Hive metastore tables and views.

• Auto Loader using the read_files() function to read from Unity Catalog external locations.

• Apache Kafka and Amazon Kinesis.

The following are examples of reading from Unity Catalog and Hive metastore tables.

Batch ingestion from a Unity Catalog table

CREATE OR REFRESH MATERIALIZED VIEW
  table_name
AS SELECT
  *
FROM
  my_catalog.my_schema.table1;

@dlt.table
def table_name():
  return spark.table("my_catalog.my_schema.table")

Stream changes from a Unity Catalog table

CREATE OR REFRESH STREAMING TABLE
  table_name
AS SELECT
  *
FROM
  STREAM(my_catalog.my_schema.table1);

@dlt.table
def table_name():
  return spark.readStream.table("my_catalog.my_schema.table")

Ingest data from Hive metastore

A pipeline that uses Unity Catalog can read data from Hive metastore tables using the hive_metastore catalog:

CREATE OR REFRESH MATERIALIZED VIEW
  table_name
AS SELECT
  *
FROM
  hive_metastore.some_schema.table;

@dlt.table
def table3():
  return spark.table("hive_metastore.some_schema.table")

Ingest data from Auto Loader

CREATE OR REFRESH STREAMING TABLE
  table_name
AS SELECT
  *
FROM
  read_files(
    <path-to-uc-external-location>,
    "json"
  )

@dlt.table(table_properties={"quality": "bronze"})
def table_name():
  return (
     spark.readStream.format("cloudFiles")
     .option("cloudFiles.format", "json")
     .load(f"{path_to_uc_external_location}")
 )

Share materialized views

By default, only the pipeline owner has permission to query datasets created by the pipeline. You can give other users the ability to query a table by using GRANT statements and you can revoke query access using REVOKE statements. For more information about privileges in Unity Catalog, see Manage privileges in Unity Catalog.

Grant select on a table

GRANT SELECT ON TABLE
  my_catalog.my_schema.table_name
TO
  `user@databricks.com`

Revoke select on a table

REVOKE SELECT ON TABLE
  my_catalog.my_schema.table_name
FROM
  `user@databricks.com`

Grant create table or create materialized view privileges

GRANT CREATE { MATERIALIZED VIEW | TABLE } ON SCHEMA
  my_catalog.my_schema
TO
  { principal | user }

View lineage for a pipeline

Lineage for tables in a Delta Live Tables pipeline is visible in Catalog Explorer. The Catalog Explorer lineage UI shows the upstream and downstream tables for materialized views or streaming tables in a Unity Catalog-enabled pipeline. To learn more about Unity Catalog lineage, see Capture and view data lineage using Unity Catalog.

For a materialized view or streaming table in a Unity Catalog-enabled Delta Live Tables pipeline, the Catalog Explorer lineage UI will also link to the pipeline that produced the materialized view or streaming table if the pipeline is accessible from the current workspace.

Add, change, or delete data in a streaming table

You can use data manipulation language (DML) statements, including insert, update, delete, and merge statements, to modify streaming tables published to Unity Catalog. Support for DML queries against streaming tables enables use cases such as updating tables for compliance with the General Data Protection Regulation (GDPR).

Note

• DML statements that modify the table schema of a streaming table are not supported. Ensure that your DML statements do not attempt to evolve the table schema.

• DML statements that update a streaming table can be run only in a shared Unity Catalog cluster or a SQL warehouse using Databricks Runtime 13.3 LTS and above.

• Because streaming requires append-only data sources, if your processing requires streaming from a source streaming table with changes (for example, by DML statements), set the skipChangeCommits flag when reading the source streaming table. When skipChangeCommits is set, transactions that delete or modify records on the source table are ignored. If your processing does not require a streaming table, you can use a materialized view (which does not have the append-only restriction) as the target table.

The following are examples of DML statements to modify records in a streaming table.

Delete records with a specific ID:

DELETE FROM my_streaming_table WHERE id = 123;

Update records with a specific ID:

UPDATE my_streaming_table SET name = 'Jane Doe' WHERE id = 123;

Publish tables with row filters and column masks

Preview

This feature is in Public Preview.

Row filters let you specify a function that applies as a filter whenever a table scan fetches rows. These filters ensure that subsequent queries only return rows for which the filter predicate evaluates to true.

Column masks let you mask a column’s values whenever a table scan fetches rows. Future queries for that column return the evaluated function’s result instead of the column’s original value. For more information on using row filters and column masks, see Filter sensitive table data using row filters and column masks.

Managing Row Filters and Column Masks

Row filters and column masks on materialized views and streaming tables should be added, updated, or dropped through the CREATE OR REFRESH statement.

For detailed syntax on defining tables with row filters and column masks, see Delta Live Tables SQL language reference and Delta Live Tables Python language reference.

Behavior

The following are important details when using row filters or column masks in Delta Live Tables pipelines:

• Refresh as owner: When a pipeline update refreshes a materialized view or streaming table, row filter and column mask functions run with the pipeline owner’s rights. This means the table refresh uses the security context of the user who created the pipeline. Functions that check user context (such as CURRENT_USER and IS_MEMBER) are evaluated using the pipeline owner’s user context.

• Query: When querying a materialized view or streaming table, functions that check user context (such as CURRENT_USER and IS_MEMBER) are evaluated using the invoker’s user context. This approach enforces user-specific data security and access controls based on the current user’s context.

• When creating materialized views over source tables that contain row filters and column masks, the refresh of the materialized view is always a full refresh. A full refresh reprocesses all data available in the source with the latest definitions. This process checks that security policies on the source tables are evaluated and applied with the most up-to-date data and definitions.

Observability

Use DESCRIBE EXTENDED, INFORMATION_SCHEMA, or the Catalog Explorer to examine the existing row filters and column masks that apply to a given materialized view or streaming table. This functionality allows users to audit and review data access and protection measures on materialized views and streaming tables.