# Defining Databricks connection parameters with Spark Structured Streaming [Preview](https://help.qlik.com/en-US/cloud-services/Subsystems/Hub/Content/Sense_Hub/Introduction/feature-preview-program-overview.htm) Configure a Databricks connection for a Spark Structured Streaming Job. ## Procedure 1. Click the Run view beneath the design workspace, then click the Structured Streaming configuration view. 2. Select Built-in from the Property type list. 3. Select Universal from the Distribution list, select the Spark version, then select Databricks from the Runtime mode/environment list. 4. Select the Define a streaming timeout (ms) check box and, in the field that is displayed, enter the time frame at the end of which the Spark Structured Streaming Job automatically stops running. 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. | | Use DBFS to upload dependencies | Select this check box to upload dependencies to DBFS. | | 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. | | 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 for your Spark Structured Streaming Job. Information noteImportant: You must define the tuning parameters otherwise you can get an error (400 - Bad request). 7. In the Advanced properties table, add any Spark properties you want to override the defaults set by Talend Studio. ## Results The connection details are complete.