tDataDecrypt properties for Apache Spark Streaming - 7.3

Data privacy

Version
7.3
Language
English (United States)
Product
Talend Big Data Platform
Talend Data Fabric
Talend Data Management Platform
Talend Data Services Platform
Talend MDM Platform
Talend Real-Time Big Data Platform
Module
Talend Studio
Content
Data Governance > Third-party systems > Data Quality components > Data privacy components
Data Quality and Preparation > Third-party systems > Data Quality components > Data privacy components
Design and Development > Third-party systems > Data Quality components > Data privacy components

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

The Standard tDataDecrypt component belongs to the Data Quality family.

The component in this framework is available in Talend Data Management Platform, Talend Big Data Platform, Talend Real Time Big Data Platform, Talend Data Services Platform, Talend MDM Platform and in Talend Data Fabric.

Basic settings

Password

Enter the password used to encrypt the cryptographic file generated by the tDataEncrypt component.

This value must be enclosed in double quotes.

Cryptographic file path

Enter the path to the cryptographic file used to encrypt the input data with the tDataEncrypt component.

This value must be enclosed in double quotes.

Decryption

Select the corresponding Decrypt check boxes to decrypt input columns.

The columns that are not selected will not be decrypted. Properly configure the output schema of the component to set the type of the columns to be decrypted to String.

If you try to decrypt unencrypted columns, the component outputs null values in those columns.

Advanced settings

tStat Catcher Statistics

Select this check box to gather the Job processing metadata at the Job level as well as at each component level.

Usage

Usage rule

This component is usually used as an intermediate component, and it requires an input component and an output component.

Spark Connection

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 Storage for Job deployment in the Spark configuration tab.
    • When using Qubole, add a tS3Configuration to your Job to write your actual business data in the S3 system with Qubole. Without tS3Configuration, this business data is written in the Qubole HDFS system and destroyed once you shut down your cluster.
    • When using on-premise 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.

    If you are using Databricks without any configuration component present in your Job, your business data is written directly in DBFS (Databricks Filesystem).

This connection is effective on a per-Job basis.