# tDataEncrypt properties for Apache Spark Batch These properties are used to configure tDataEncrypt running in the Spark Batch Job framework. The Standard tDataEncrypt 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, and in Talend Data Fabric. ## Basic settings | Schema and Edit Schema | 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 line when naming the
fields.

Click Sync
columns to retrieve the schema from the previous component connected in the
Job.

Click Edit
schema to make changes to the schema. If the current schema is of the Repository type, three options are available:

View schema: choose this option to view the schema only.
Change to built-in property: choose this option to change the schema to Built-in for local changes.
Update repository connection: choose this option to change the schema stored in the repository and decide whether to propagate the changes to all the Jobs upon completion. If you just want to propagate the changes to the current Job, you can select No upon completion and choose this schema metadata again in the Repository Content window.

| | --- | --- | | | Built-In: You create and store the schema locally for this component
only. | | | Repository: You have already created the schema and stored it in the
Repository. You can reuse it in various projects and Job designs. | | Password | This value must be enclosed in double
quotes.

When using an existing cryptographic
file, enter the password required to use this file.

When generating a cryptographic file,
enter the password used to encrypt this file.

This password is required to decrypt
back data using the tDataDecrypt
component. | | Cryptographic file path | When using an existing cryptographic file, enter the
path to the cryptographic file. It must be enclosed in double
quotes.

When generating a cryptographic file, enter the destination file path.
You can enter different types of values:

A context: context.mycontext
To generate the file in local, enclose the path in double quotes: "C:/user/cryptofiles/mycryptofile"
To generate the file on a cluster, enclose the HDFS path in double quotes: "hdfs://repository/cryptofiles/mycryptofile"

This cryptographic file is encrypted
using AES-GCM.

This cryptographic file is required to
decrypt back data using the tDataDecrypt
component.

For more information about the cryptographic file, see
the [data
encryption process](https://help.qlik.com/talend/en-US/components/7.3/data-privacy/tdataencrypt). | | Generate cryptographic file

![](https://help.qlik.com/talend/en-US/components/7.3/Resources/images/tdataencrypt_generate_icon.png) | Click this button to generate the
cryptographic file.

In the dialog box, select the
cryptographic method used to encrypt the input data:

AES, which is a 128-bit block cipher standardized by the National Institute of Standards and Technology (NIST).
Blowfish, which is a 64-bit unpatented block cipher developed by Bruce Schneier.

| | Encryption | Select the corresponding Encrypt check boxes to encrypt input
columns.

You can encrypt all column data
types, except Dynamic, but the output
encrypted data is of String type.

Configure the output schema of the
component to set the type of the columns to be encrypted to String.

The columns that are not selected
will not be encrypted and be returned as-is by the component. | ## 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-premises 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 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).

This connection is effective on a per-Job basis. |