# Defining Local connection parameters with Spark Universal

## About this task

Talend Studio connects to your local file system on your machine to run the Job. Talend Studio builds the Spark environment in itself at runtime.

## 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 Local from the Runtime mode/environment drop-down list.
4. If you need to launch your Spark Job from Windows, specify where the *winutils.exe* program to be used is stored:
   * If you know where to find your *winutils.exe* file and you want to use it, select the Define the Hadoop home directory check box and enter the directory where your *winutils.exe* is stored.

   * Otherwise, leave the Define the Hadoop home directory check box clear, Talend Studio generates one by itself and automatically uses it for this Job.
5. Enter the basic configuration information:
   | Parameter | Usage |
   | --- | --- |
   | Use local timezone | Select this check box to let Spark use the local time<br>zone provided by the system.<br><br>Information noteNote:<br><br><ul> <li>If you clear this check box, Spark use UTC time                                                  zone.</li> <li>Some components also have the <span>Use                                                  local timezone for date</span> check box. If                                                  you clear the check box from the component, it                                                  inherits time zone from the Spark                                                  configuration.</li> </ul> |
   | Use dataset API in migrated<br>components | Select this check box to let the components use Dataset<br>(DS) API instead of Resilient Distributed Dataset (RDD) API:<br><br><ul> <li>If you select the check box, the components inside                                                the Job run with DS which improves performance.</li> <li>If you clear the check box, the components inside                                                the Job run with RDD which means the Job remains                                                unchanged. This ensure the backwards                                                compatibility.</li> </ul><br><br>This check box is selected by default, but if you import a Job from 7.3 backwards, the<br>check box will be cleared as those Jobs run with RDD.<br><br>Information noteImportant: If your Job contains<br>tDeltaLakeInput and tDeltaLakeOutput components, you<br>must select this check box. |
   | Use timestamp for dataset components | Select this check box to use<br>java.sql.Timestamp for dates.<br><br>Information noteNote: If<br>you leave this check box clear,<br>java.sql.Timestamp or<br>java.sql.Date can be used depending<br>on the pattern. |
   | Parallelize output files writing | Select this checkbox to enable the Spark Batch Job to run multiple<br>threads in parallel when writing output files. This option improves the<br>performance of the execution time.<br><br>When you leave<br>this checkbox cleared, the output files are written sequentially<br>within one thread.<br><br>On subJobs level, each subJob is treated<br>sequentially. Only the output file inside the subJob is<br>parallelized.<br><br>This option is only available for Spark Batch<br>Jobs containing the following output components:<br><br><ul> <li>tAvroOutput</li> <li>tFileOutputDelimited (only when the <span>Use dataset                                            API in migrated components</span> checkbox is                                        selected)</li> <li>tFileOutputParquet</li> </ul><br><br>Information noteImportant: To avoid memory<br>problems during the execution of the Job, you need to take into<br>account the size of the files being written and the execution<br>environment capacity before using this<br>parameter. |
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 are complete, you are ready to schedule executions of your Spark Job or to run it immediately.