Use DLT pipelines with legacy Hive metastore
This article details configurations and caveats specific to DLT pipelines configured to publish data to the legacy Hive metastore. Databricks recommends using Unity Catalog for all new pipelines. See Use Unity Catalog with your DLT pipelines.
Note
This article discusses functionality for the current default publishing mode for pipelines. Pipelines created before February 5, 2025, might use the legacy publishing mode and LIVE virtual schema. See LIVE schema (legacy).
How to query streaming tables and materialized views in the legacy Hive metastore
After an update is complete, you can view the schema and tables, query the data, or use the data in downstream applications.
Once published, DLT tables can be queried from any environment with access to the target schema. This includes Databricks SQL, notebooks, and other DLT pipelines.
Important
When you create a target configuration, only tables and associated metadata are published. Views are not published to the metastore.
Configure a pipeline to publish to Hive metastore
When creating a new pipeline, you can specify Hive metastore under the Storage options to publish to the legacy Hive metastore. You must specify a default target schema when publishing to Hive metastore. See Configure a DLT pipeline.
Specify a storage location
You can specify a storage location for a pipeline that publishes to the Hive metastore. The primary motivation for specifying a location is to control the object storage location for data written by your pipeline. Databricks recommends always specificying a storage location to avoid writing to the DBFS root.
Because all tables, data, checkpoints, and metadata for DLT pipelines are fully managed by DLT, most interaction with DLT datasets happens through tables registered to the Hive metastore or Unity Catalog.
Cloud storage configuration
To access Azure storage, you must configure required parameters, including access tokens, using spark.conf settings in your cluster configurations. For an example of configuring access to an Azure Data Lake Storage (ADLS) storage account, see Securely access storage credentials with secrets in a pipeline.
Work with event log for Hive metastore pipelines
If your pipeline publishes tables to the Hive metastore, the event log is stored in /system/events under the storage location. For example, if you have configured your pipeline storage setting as /Users/username/data, the event log is stored in the /Users/username/data/system/events path in DBFS.
If you have not configured the storage setting, the default event log location is /pipelines/<pipeline-id>/system/events in DBFS. For example, if the ID of your pipeline is 91de5e48-35ed-11ec-8d3d-0242ac130003, the storage location is /pipelines/91de5e48-35ed-11ec-8d3d-0242ac130003/system/events.
You can create a view to simplify querying the event log. The following example creates a temporary view called event_log_raw. This view is used in the example event log queries included in this article:
CREATE OR REPLACE TEMP VIEW event_log_raw
AS SELECT * FROM delta.`<event-log-path>`;
Replace <event-log-path> with the event log location.
Each instance of a pipeline run is called an update. You often want to extract information for the most recent update. Run the following query to find the identifier for the most recent update and save it in the latest_update_id temporary view. This view is used in the example event log queries included in this article:
CREATE OR REPLACE TEMP VIEW latest_update AS
SELECT origin.update_id AS id
FROM event_log_raw
WHERE event_type = 'create_update'
ORDER BY timestamp DESC
LIMIT 1;
You can query the event log in an Azure Databricks notebook or the SQL editor. Use a notebook or the SQL editor to run the example event log queries.
Example pipeline source code notebooks for workspaces without Unity Catalog
You can import the following notebooks into a Azure Databricks workspace without Unity Catalog enabled and use them to deploy a DLT pipeline. Import the notebook of your chosen language and specify the path in Source code field when configuring a pipeline with the Hive metastore storage option. See Configure a DLT pipeline.