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.
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Yarn mode (Yarn client or Yarn cluster):
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When using Google Dataproc, specify a bucket in the Google Storage staging bucket field in the Spark configuration tab.
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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 Storage for Job deployment in the Spark configuration tab.
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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 tHDFSConfiguration.
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Standalone mode: use the configuration component corresponding to the file system your cluster is using, such as tHDFSConfiguration Apache Spark Batch or tS3Configuration Apache Spark Batch.
If you are using Databricks without any configuration component present in your Job, your business data is written directly in DBFS (Databricks Filesystem).