Configure Databricks as a target

note

Databricks is currently available as a preview feature and under development. If you use Databricks as a target metastore with Data Migrator, and have feedback to share, please contact Support.

This feature currently does not support migrating transactional ACID tables, or INSERT_ONLY transactional tables regardless conversion options.

The feature is automatically enabled.

Constraints

Migrations that include Hive constraints are not supported.

Configure a Databricks metadata agent

Use Data Migrator to integrate with Databricks and migrate structured data from Hadoop to Databricks tables, including converting automatically from source Hive formats to Delta Lake format used in Databricks.

Configure a Databricks metadata agent in Data Migrator using the UI or CLI, and connect it to your Databricks cluster.

Prerequisites

To ensure a successful migration to Databricks, the source tables must be in one of the following formats:

• CSV
• JSON
• AVRO
• ORC
• PARQUET
• Text

Ensure you have the following before you start:

• A Databricks cluster
• A Databricks File System (DBFS)
• Cloud storage mounted onto the DBFS
• Install Databricks driver

Example: Script to mount ADLS Gen2 or blob storage with Azure Blob File System

configs = {"fs.azure.account.auth.type": "OAuth",
       "fs.azure.account.oauth.provider.type": "org.apache.hadoop.fs.azurebfs.oauth2.ClientCredsTokenProvider",
       "fs.azure.account.oauth2.client.id": "<application-id>",
       "fs.azure.account.oauth2.client.secret": dbutils.secrets.get(scope="<scope-name>",key="<service-credential-key-name>"),
       "fs.azure.account.oauth2.client.endpoint": "https://login.microsoftonline.com/<directory-id>/oauth2/token"}

# Optionally, you can add example-directory-name to the source URI of your mount point.
dbutils.fs.mount(
source = "abfss://<container-name>@<storage-account-name>.dfs.core.windows.net/",
mount_point = "/mnt/<mount-name>",
extra_configs = configs)

Replace:

• <application-id> with the Application (client) ID for the Azure Active Directory application.
• <scope-name> with the Databricks secret scope name.
• <service-credential-key-name> with the name of the key containing the client secret.
• <directory-id> with the Directory (tenant) ID for the Azure Active Directory application.
• <container-name> with the name of a container in the ADLS Gen2 storage account.
• <storage-account-name> with the ADLS Gen2 storage account name.
• <mount-name> with the name of the intended mount point in DBFS.

Install Databricks driver

To install the JDBC driver:

1. Download the Databricks JDBC driver.

   note

   Data Migrator only supports JDBC driver version 2.6.25 or higher.

2. Unzip the package and upload the DatabricksJDBC42.jar file to the Data Migrator host machine.

3. Move the DatabricksJDBC42.jar file to the Data Migrator directory:

   /opt/wandisco/hivemigrator/agent/databricks

4. Change ownership of the jar file to the Hive Migrator system user and group:

   chown hive:hadoop /opt/wandisco/hivemigrator/agent/databricks/DatabricksJDBC42.jar

Configure Databricks as a target with the UI

To add Databricks from your Dashboard:

1. From the Dashboard, select a product under Products.

2. Under Filesystems & Agents, select Metastore Agents.

3. Select Connect to Metastore.

4. Select the filesystem.

5. Select Databricks as the Metastore Type.

6. Enter a Display Name.

7. Enter the JDBC Server Hostname, Port, and HTTP Path.

8. Enter the Databricks Access Token.

   note

   You’ll need to reenter the access token when updating this agent.

9. (Optional) - Enter the name of your Databricks Unity Catalog.

   info

   You can't update an agent's Unity Catalog while it's in an active migration.

10. Select Convert to Delta Lake if you want to convert your tables.

11. Select Delete after conversion to delete the underlying table data and metadata from the Filesystem Mount Point location after it has been converted to Delta Lake in Databricks.

    info

    Only use this option if you're performing one-time migrations for the underlying table data. The Databricks agent doesn't support continuous (live) updates of table data if you're converting to Delta Lake in Databricks.

12. Enter the Filesystem Mount Point.
    The filesystem that contains your data you want to migrate must be mounted onto your DBFS.
    Enter the mounted container's path on the DBFS.

    Example: Mounted container's path

    /mounted/container/path

13. Enter path for Default Filesystem Override.

    1. If you select Convert to Delta Lake , enter the location on the DBFS to store the tables converted to Delta Lake. To store Delta Lake tables on cloud storage, enter the path to the mount point and the path on the cloud storage.

       Example: Location on the DBFS to store tables converted to Delta Lake

       dbfs:<location>
       Example: Cloud storage location

       dbfs:/mnt/adls2/storage_account/delta_tables

    2. If you don't select Convert to Delta, enter the mount point.

       Example: Filesystem mount point

       dbfs:<value of Fs mount point>

14. Select Save.

Next steps

Create a metadata migration.

info

Under certain conditions with a Databricks target, source truncate operations may take longer than expected. See the following Knowledge base article for more information.

Configure Databricks as a target with the CLI

To add Databricks as a metadata agent with the CLI, use the commands below and refer to the example.

Example: Configure a Databricks metadata agent with the CLI

hive agent add databricks --name databricksAgent --jdbc-server-hostname mydbcluster.cloud.databricks.com  --jdbc-port 443 --jdbc-http-path sql/protocolv1/o/8445611123456789/0234-125567-testy978 --access-token daexamplefg123456789t6f0b57dfdtoken4 --file-system-id mys3bucket --default-fs-override dbfs:/mnt/mybucketname --fs-mount-point /mnt/mybucket --convert-to-delta

Command	Action
hive agent add databricks	Add an agent for a Databricks metastore.
hive agent configure databricks	Update the configuration of an existing agent for the Databricks metastore.
hive agent check	Check whether the agent can connect to the metastore.
hive agent delete	Delete an agent.
hive agent list	List all configured agents.
hive agent show	View the configuration for an agent.
hive agent types	List supported agent types.

hive agent add databricks and hive agent configure databricks

Mandatory parameters

Parameter	Description
--name	The ID to give to the new Hive agent.
--jdbc-server-hostname	The JDBC server hostname for the Databricks cluster (AWS, Azure, or GCP).
--jdbc-port	The port used for JDBC connections to the Databricks cluster (AWS, Azure, or GCP).
--jdbc-http-path	The HTTP path for the Databricks cluster (AWS, Azure, or GCP).
--access-token	The personal access token to be used for the Databricks cluster (AWS, Azure, or GCP).
--file-system-id	Enter a name for the filesystem you want to associate with this agent (for example, myadls2 or mys3bucket). This ensures any path mappings are correctly linked between the filesystem and the agent.
--default-fs-override	Enter an override for the default filesystem URI instead of a filesystem name (for example, dbfs:).

note

Use either the --file-system-id or the --default-fs-override parameter but not both. To convert to Delta Lake format, use the --convert-to-delta parameter. Set the value of the --default-fs-override parameter to dbfs: or a path in the Databricks filesystem. For example, dbfs:/mount/externalStorage.

Optional parameters

Parameter	Description
--fs-mount-point	Define the location in the Databricks filesystem to mount the filesystem (like Azure Data Lake Storage Gen2, S3, or Google Cloud Storage) that contains the data you want to migrate. If you use the --convert-to-delta option, the data is converted and placed in the location on the Databricks filesystem defined by --default-fs-override. If you don't use the --convert-to-delta option, the data is migrated directly from the location defined by the --fs-mount-point.
--convert-to-delta	All underlying table data and metadata is migrated to the filesystem location defined by the --fs-mount-point parameter. Use this option to convert the associated data and metadata to Delta Lake format in Databricks (AWS, Azure or GCP).
--delete-after-conversion	Use this parameter if you used the parameter --convert-to-delta. --delete-after-conversion deletes the underlying table data and metadata from the filesystem location (defined by --fs-mount-point) after it has been converted to Delta Lake in Databricks. Only use this option if you're performing one-time migrations for the underlying table data. The Databricks agent doesn't support continuous (live) updates of table data if you're converting to Delta Lake in Databricks.
--catalog	Enter the name of your Databricks Unity Catalog.

note

To ensure you see all of your migrated data in Databricks, set the value of default-fs-override to dbfs:/path/ and replace /path/ with the value from the --fs-mount-point parameter.

--default-fs-override dbfs:/mnt/mybucketname

Next steps

Create a metadata migration with the CLI using the Databricks target agent you just configured.