Transforming data in a Spark environment

Transforming data in a Spark environment - 7.0

Data mapping

EnrichVersion

7.0

EnrichProdName

Talend Big Data Platform

Talend Data Fabric

Talend Data Management Platform

Talend Data Services Platform

Talend MDM Platform

Talend Real-Time Big Data Platform

EnrichPlatform

Talend Studio

task

Data Governance > Third-party systems > Processing components (Integration) > Data mapping

Data Quality and Preparation > Third-party systems > Processing components (Integration) > Data mapping

Design and Development > Third-party systems > Processing components (Integration) > Data mapping

Subscription

This scenario applies only to subscription-based Talend Platform products with Big Data and Talend Data Fabric.

For more technologies supported by Talend, see Talend components.

The following scenario creates a two-component Job that transforms data in a Spark environment using a map that was previously created in Talend Data Mapper .

tHDFSConfiguration is used in this scenario by Spark to connect to the HDFS system where the JAR files dependent on the Job are transferred.

In the Spark Configuration tab in the Run view, define the connection to a given Spark cluster for the whole Job. In addition, since the Job expects its dependent JAR files for execution, you must specify the directory in the file system to which these JAR files are transferred so that Spark can access these files:

Yarn mode (YARN client or YARN cluster):
- When using Google Dataproc, specify a bucket in the Google Storage staging bucket field in the Spark configuration tab.
- When using HDInsight, specify the blob to be used for Job deployment in the Windows Azure Storage configuration area in the Spark configuration tab.
- When using Altus, specify the S3 bucket or the Azure Data Lake store (technical preview) for Job deployment in the Spark configuration tab.
- When using other distributions, use the configuration component corresponding to the file system your cluster is using. Typically, this system is HDFS and so use tHDFSConfiguration.
Standalone mode: use the configuration component corresponding to the file system your cluster is using, such as tHDFSConfiguration or tS3Configuration.