# tDynamoDBOutput properties for Apache Spark Streaming (deprecated)

Availability-note[Deprecated](https://help.talend.com/r/en-US/Cloud/labels-on-talend-help-center/availability-deprecated)

This content is deprecated.

These properties are used to configure tDynamoDBOutput running in the Spark Streaming Job framework.

Information noteDeprecation: Spark Streaming is deprecated beginning with the R2026-09 release. Use Spark Structured Streaming instead.

The Spark Streaming tDynamoDBOutput component belongs to the Databases family.

This component is available in Talend Real-Time Big Data Platform and Talend Data Fabric.

## Basic settings

| Properties | Description |
| --- | --- |
| Use an existing connection | Select this check box and in the Component<br>List drop-down list, select the desired connection component to<br>reuse the connection details you already defined. |
| Access<br>Key | Enter the access<br>key ID that uniquely identifies an AWS Account. For further information about how to<br>get your Access Key and Secret Key, see [Getting Your AWS Access<br>Keys](http://docs.aws.amazon.com/ses/latest/DeveloperGuide/get-aws-keys.html). |
| Secret<br>Key | Enter the secret access key,<br>constituting the security credentials in combination with the access Key.<br><br>To enter the secret key, click the [...]<br>button next to the secret key field, and then in the pop-up dialog box enter the password<br>between double quotes and click OK to save the<br>settings. |
| Use End Point | Select this check box and in the Server URL<br>field displayed, specify the Web service URL of the DynamoDB database service. |
| Region | Specify the AWS region by selecting or entering a region name between double<br>quotation marks (e.g. *"us-east-1"*). For more information about the<br>AWS Region, see [Regions and Endpoints](http://docs.aws.amazon.com/general/latest/gr/rande.html#s3_region). |
| Schema and Edit<br>schema | <ul> <li> <p>A schema is a row description. It defines the number of fields            (columns) to be processed and passed on to the next component. When you create a Spark            Job, avoid the reserved word <kbd>line</kbd> when naming the            fields.</p> <ul> <li> <p> <span>Built-In</span>: You create and store the schema locally for this component                only.</p> </li> <li> <p> <span>Repository</span>: You have already created the schema and stored it in the                Repository. You can reuse it in various projects and Job designs.</p> </li> </ul> </li> <li> <p>Click <span>Edit        schema</span> to make changes to the schema. If the current schema is of the <span>Repository</span> type, three options are available:</p> <ul> <li> <p> <span>View schema</span>: choose this                option to view the schema only.</p> </li> <li> <p> <span>Change to built-in property</span>:                choose this option to change the schema to <span>Built-in</span> for local changes.</p> </li> <li> <p> <span>Update repository connection</span>:                choose this option to change the schema stored in the repository and decide whether                to propagate the changes to all the Jobs upon completion.</p> <p>If you just want to                propagate the changes to the current Job, you can select <span>No</span> upon completion and choose this schema metadata                again in the <span>Repository Content</span>                window.</p> </li> </ul> </li> </ul> |
| Table Name | Specify the name of the table in which you need to write data. This table must already<br>exist. |
| Die on error | Select the checkbox to stop the execution of the Job when an error<br>occurs. |

## Advanced settings

| Properties | Description |
| --- | --- |
| Throughput write percent | Enter, without using quotation marks, the percentage (expressed in decimal) to be used of<br>the write capacity pre-defined in Amazon. For further information about this write capacity,<br>see [Provision throughput for write](http://docs.aws.amazon.com/amazondynamodb/latest/developerguide/HowItWorks.ProvisionedThroughput.html#HowItWorks.ProvisionedThroughput.Writes). |
| Advanced properties | Add properties to define extra operations you need tDynamoDBOutput to perform when writing data.<br><br>This table is present for future evolution of the component and using it requires the<br>high-level knowledge of DynamoDB development. Currently, there are no interesting user<br>configurable properties. |

## Usage

| Usage guidance | Description |
| --- | --- |
| **Usage rule** | This component is used as an end component and requires an input link.<br><br>This component should use a tDynamoDBConfiguration<br>component present in the same Job to connect to a DynamoDB database. You need to drop a<br>tDynamoDBConfiguration component alongside this<br>component and configure the Basic settings of this<br>component to use tDynamoDBConfiguration.<br><br>This component, along with the Spark Streaming component Palette it belongs to, appears<br>only when you are creating a Spark Streaming Job.<br><br>Note that in this documentation, unless otherwise explicitly stated, a scenario presents<br>only Standard Jobs, that is to say traditional<br>Talend<br>data<br>integration Jobs. |
| Spark Connection | In the Spark<br>Configuration tab in the Run<br>view, define the connection to a given Spark cluster for the whole Job. In<br>addition, since the Job expects its dependent jar files for execution, you must<br>specify the directory in the file system to which these jar files are<br>transferred so that Spark can access these files:<br><br><ul> <li> <div> <span>Yarn mode</span> (Yarn client or Yarn cluster):<ul> <li> <p>When using Google Dataproc, specify a bucket in the                                            <span>Google Storage staging bucket</span>                                        field in the <span>Spark configuration</span>                                        tab.</p> </li> <li> <p>When using HDInsight, specify the blob to be used for Job                                        deployment in the <span>Windows Azure Storage                                            configuration</span> area in the <span>Spark                                            configuration</span> tab.</p> </li> <li>When using Altus, specify the S3 bucket or the Azure                                    Data Lake Storage for Job deployment in the <span>Spark                                        configuration</span> tab.</li> <li> <p>When using on-premises distributions, use                                        the configuration component corresponding to the file system                                        your cluster is using. Typically, this system is HDFS and so                                        use <a href="https://help.qlik.com/talend/r/en-US/8.0/components/hdfs/thdfsconfiguration">tHDFSConfiguration</a>.</p> </li> </ul> </div> </li> <li> <p> <span>Standalone mode</span>: use the                            configuration component corresponding to the file system your cluster is                            using, such as <a href="https://help.qlik.com/talend/r/en-US/8.0/components/hdfs/thdfsconfiguration-properties-for-apache-spark-batch">tHDFSConfiguration Apache Spark Batch</a> or <a href="https://help.qlik.com/talend/r/en-US/8.0/components/amazon-s3/ts3configuration-properties-for-apache-spark-batch">tS3Configuration Apache Spark Batch</a>.</p> <p>If you are using Databricks without any configuration component present                            in your Job, your business data is written directly in DBFS (Databricks                            Filesystem).</p> </li> </ul><br><br>This connection is effective on a per-Job basis. |