Applying a preparation to a data sample in an Apache Spark Batch Job - 7.0

Data Preparation

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Data Governance > Third-party systems > Data Preparation components
Data Quality and Preparation > Third-party systems > Data Preparation components
Design and Development > Third-party systems > Data Preparation components

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

For more technologies supported by Talend, see Talend components.

The tDataprepRun component allows you to reuse an existing preparation made in Talend Data Preparation, directly in a Big Data Job. In other words, you can operationalize the process of applying a preparation to input data with the same model.

The following scenario creates a simple Job that :

  • Reads a small sample of customer data,
  • applies an existing preparation on this data,
  • shows the result of the execution in the console.

This assumes that a preparation has been created beforehand, on a dataset with the same schema as your input data for the Job. In this case, the existing preparation is called datapreprun_spark. This simple preparation puts the customer last names into upper case and applies a filter to isolate the customers from California, Texas and Florida.

The sample data reads as follows:
Todd;Lane;New Jersey
Note: The sample data is created for demonstration purposes only.

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.

Prerequisite: ensure that the Spark cluster has been properly installed and is running.