# Defining Databricks connection parameters with Spark Universal ## About this task Talend Studio connects to an all-purpose Databricks cluster to run the Job from this cluster. ## Procedure 1. Click the Run view beneath the design workspace, then click the Spark configuration view. 2. Select Built-in from the Property type drop-down list. If you have already set up the connection parameters in the Repository as explained in [Centralizing a Hadoop connection](https://help.qlik.com/talend/en-US/studio-user-guide/8.0-R2025-07/Centralizing%20a%20Hadoop%20connection/talend/en-US/studio-user-guide/8.0/centralizing-hadoop-connection), you can easily reuse it. To do this, select Repository from the Property type drop-down list, then click […] button to open the Repository Content dialog box and select the Hadoop connection to be used. Information noteTip: Setting up the connection in the Repository allows you to avoid configuring that connection each time you need it in the Spark configuration view of your Jobs. The fields are automatically filled. 3. Select Universal from the Distribution drop-down list, the Spark version from the Version drop-down list, and Databricks from the Runtime mode/environment drop-down list. 4. Enter the basic configuration information: | Parameter | Usage | | --- | --- | | Use local timezone | Select this check box to let Spark use the local time zone
provided by the system.

Information noteNote:

| | Use dataset API in migrated components | Select this check box to let the components use Dataset (DS) API
instead of Resilient Distributed Dataset (RDD) API:



This check box is selected by default, but if you import a Job from 7.3 backwards, the
check box will be cleared as those Jobs run with RDD.

Information noteImportant: If your Job contains tDeltaLakeInput and
tDeltaLakeOutput components, you must select this check box. | | Use timestamp for dataset components | Select this check box to use java.sql.Timestamp
for dates.

Information noteNote: If you leave this check box clear,
java.sql.Timestamp or
java.sql.Date can be used depending on the
pattern. | | Parallelize output files writing | Select this checkbox to enable the Spark Batch Job to run multiple
threads in parallel when writing output files. This option improves the
performance of the execution time.

When you leave
this checkbox cleared, the output files are written sequentially
within one thread.

On subJobs level, each subJob is treated
sequentially. Only the output file inside the subJob is
parallelized.

This option is only available for Spark Batch
Jobs containing the following output components:



Information noteImportant: To avoid memory
problems during the execution of the Job, you need to take into
account the size of the files being written and the execution
environment capacity before using this
parameter. | 5. Complete the Databricks configuration parameters: | Parameter | Usage | | --- | --- | | Cloud provider | Select the cloud provider to be used between
AWS, Azure
and GCP. | | Run mode | Select the mode you want to use to run your Job on
Databricks cluster when you execute your Job in Talend Studio. With Create and run now, a new
Job is created and run immediately on Databricks and with
Runs submit, a one-time run is
submitted without creating a Job on Databricks. | | Enable Unity Catalog | Select this check box to leverage Unity Catalog. Then, you need to
specify the Unity Catalog related information in the
Catalog, Schema, and
Volume parameters.

Information noteImportant: All
the parameters have to be created on Databricks with granted
permissions for all authorized users before using them in Talend Studio. | | Use pool | You can select this check box to leverage a Databricks pool. If
you do, you must indicate the Pool ID instead
of the Cluster ID. You must also select
Job clusters from the Cluster
type drop-down list. | | Endpoint | Enter the URL address of your workspace. | | Cluster ID | Enter the ID of the Databricks cluster to be used. This
ID is the value of the
spark.databricks.clusterUsageTags.clusterId
property of your Spark cluster. You can find this property
on the properties list in the
Environment tab in the
Spark UI view of your cluster. | | Authentication mode | Select the method you want to use to authentication in the
drop-down list:

| | Authentication token | Enter the authentication token generated for your
Databricks user account. | | Dependencies folder | Enter the directory that is used to store your Job related
dependencies on Databricks Filesystem at runtime, putting a slash (/)
at the end of this directory. For example, enter
/jars/ to store the dependencies in a folder
named jars. This folder is created on the fly if
it does not exist then.

From Databricks 15.4 LTS, the default
library location is moved to WORKSPACE, instead of
DBFS. | | Project ID | Enter the ID of your Google Platform project where the
Databricks project is located.

This field is only
available when you select GCP
from the Cloud provider drop-down
list. | | Bucket | Enter the name of the bucket you use for Databricks from
Google Platform.

This field is only available when you
select GCP from the
Cloud provider drop-down
list. | | Workspace ID | Enter the ID of your Google Platform workspace respecting
the following format:
databricks-workspaceid.

This
field is only available when you select
GCP from the Cloud
provider drop-down list. | | Google credentials | Enter the directory in which the JSON file containing your service
account key is stored in the Talend JobServer machine.

This field is only available when you select
GCP from the Cloud
provider drop-down list. | | Poll interval when retrieving Job status (in
ms) | Enter the time interval (in milliseconds) at the end of which you
want Talend Studio to ask Spark for the status of your Job. | | Cluster type | From the drop-down list, select the type of cluster you want to
use. For more information, see [About Databricks clusters](https://help.qlik.com/talend/en-US/components/8.0/databricks/about-databricks-clusters).

Information noteNote: When you run the Job using Talend Studio with Java 17, you need to set the
JNAME=zulu17-ca-amd64 environment
variable:



DBFS is no longer supported as Init
scripts location. For all versions of Databricks, it
is replaced to WORKSPACE. | | Enable ACL | Select this check box to use access control lists (ACLs) to
configure permission to access workspace or account level
objects.

In ACL permission, you
can configure permission to access workspace objects with
CAN\_MANAGE,
CAN\_MANAGE\_RUN,
IS\_OWNER, or
CAN\_VIEW.

In ACL
type, you can configure permission to use account-level
objects with User,
Group, or Service
Principal.

In Name, enter
the name you were given by the administrator.

This option is
available when Cluster type is set to
Job clusters. For more information, see the
[Databricks
documentation](https://docs.databricks.com/aws/en/security/auth/access-control). | | Do not restart the cluster when
submitting | Select this check box to prevent Talend Studio restarting the cluster when Talend Studio is submitting your Jobs. However, if you make changes in your Jobs,
clear this check box so that Talend Studio restarts your cluster to take these changes into account. | 6. In the Spark "scratch" directory field, enter the directory in which Talend Studio stores in the local system the temporary files such as the jar files to be transferred. If you launch the Job on Windows, the default disk is *C:*. So if you leave */tmp* in this field, this directory is *C:/tmp*. 7. If you need the Job to be resilient to failure, select the **Activate checkpointing** check box to enable the Spark checkpointing operation. In the Checkpoint directory field, enter the directory in which Spark stores, in the file system of the cluster, the context data of the computations such as the metadata and the generated RDDs of this computation. 8. In the Advanced properties table, add any Spark properties you need to use to override their default counterparts used by Talend Studio. 9. Select the Use Atlas check box to make use of Atlas to trace lineage of given data flow to discover how this data was generated by a Spark Job, including the components used in this Job and the schema changes between the components. This option is only available for Spark Universal 3.3.x. With this option activated, you need to set the following parameters: * Atlas URL: enter the location of the Atlas to be connected to. It is often *http://name\_of\_your\_atlas\_node:port* * In the Username and Password fields, enter the authentication information for access to Atlas. * Set Atlas configuration folder: if your Atlas cluster contains custom properties such as SSL or read timeout, select this check box, and in the displayed field, enter a directory in your local machine, then place the *atlas-application.properties* file of your Atlas in this directory. This way, your Job is enabled to use these custom properties. You need to ask the administrator of your cluster for this configuration file. For further information about this file, see the Client Configs section in [Atlas configuration](https://atlas.apache.org/0.8.2/Configuration.html). * Die on error: select this check box to stop the Job execution when Atlas-related issues occur, such as connection issues to Atlas. Otherwise, leave it clear to allow your Job to continue to run. ## Results The connection details to the Databricks cluster are complete, you are ready to schedule executions of your Job or to run it immediately from this cluster.