# 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-R2026-09/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 in 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 to authenticate to Databricks in the drop-down
list:

| | 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. | | Use policy | Select this check box to enter the name of the policy to be used
by your Job cluster. You can use a policy to limit the ability to
configure clusters based on a set of rules.

For more information
about cluster policies, see [Manage cluster policies](https://docs.databricks.com/administration-guide/clusters/policies.html#:~:text=A%20cluster%20policy%20limits%20the,a%20specific%20users%20and%20groups.&text=Limit%20users%20to%20create%20clusters%20with%20prescribed%20settings.)
from the official Databricks documentation. | | 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). | | Autoscale | Select or clear this check box to define the number of workers to
be used by your Job cluster. If you select this check box, autoscaling
is enabled. Then define the minimum number of workers in **Min
workers** and the maximum number of workers in **Max
workers**. Your Job cluster is scaled up and down in this scope
based on its workload.

| | Node type and Driver node
type | Select the node types for the workers and the Spark driver node.
These types determine the capacity of your nodes and their pricing by
Databricks.

For more information about these node types and the
Databricks Units they use, see [Supported Instance Types](https://databricks.com/product/aws-pricing/instance-types)
from the Databricks documentation. | | Enable credentials passthrough | Select this check box to disable user credential passthrough when
connecting to Databricks Universal. When this option is selected,
users' individual credentials are not used for authentication to data
sources. | | Number of on-demand | Select this check box to specify the maximum number of on-demand
compute resources (such as virtual machines or worker nodes). | | Spot with fall back to On-demand | Select this check box to allow the use of Spot clusters with
automatic fallback to on-demand clusters if Spot resources are
unavailable. | | Availability zone | Select this check box to specify the availability zone in which
your Databricks resources will be deployed. | | Max spot price | Select this check box to specify the maximum price you are willing
to pay per hour for Spot instances when Databricks provisions compute
resources. | | EBS volume type | Choose the type of EBS volume from the drop-down menu between
None, General purpose
SSB and Throughput optimized
HDD. | | Configure instance profile ARN | Select this check box to specify the instance profile ARN to
assign custom permissions to your Databricks resources, enabling
secure access to AWS services as needed. | | Elastic disk | Select this check box to enable your Job cluster to automatically
scale up its disk space when its Spark workers are running low on disk
space.

For more details about this elastic disk feature, search
for the section about autoscaling local storage from your
Databricks documentation. | | SSH public key | If an SSH access has been set up for your cluster, enter the
public key of the generated SSH key pair. This public key is
automatically added to each node of your Job cluster. If no SSH access
has been set up, ignore this field.

For more information about SSH
access to your cluster, see [SSH access to clusters](https://docs.databricks.com/user-guide/clusters/ssh.html)
from the official Databricks documentation. | | Configure cluster logs | Select this check box to define where to store your Spark logs for
a long term. | | Custom tags | Select this check box to add custom tags as key-value pairs to
your Databricks resources. | | Init scripts | DBFS is no longer supported as Init scripts
location. For all versions of Databricks, it was replaced to
WORKSPACE. | | 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. Select the Set tuning properties check box to define the tuning parameters, by following the process explained in [Tuning Spark for Apache Spark Batch Jobs](https://help.qlik.com/talend/en-US/components/8.0/spark-batch/tuning-spark-for-apache-spark-batch-jobs-spark-batch-component). Information noteImportant: You must define the tuning parameters otherwise you can get an error (400 - Bad request). 7. In the Spark "scratch" directory field, enter the local path where Talend Studio stores temporary files, like JARs to transfer. If you run the Job on Windows, the default disk is C:. Leaving /tmp in this field will use C:/tmp as the directory. 8. To make your Job resilient to failure, select Activate checkpointing to enable Spark’s checkpointing operation. In the Checkpoint directory field, enter the cluster file system path where Spark saves context data, such as metadata and generated RDDs. 9. In the Advanced properties table, add any Spark properties you want to override the defaults set by Talend Studio. ## 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.