# tVerifyEmail properties for Apache Spark Batch These properties are used to configure tVerifyEmail running in the Spark Batch Job framework. The Spark Batch tVerifyEmail component belongs to the Data Quality family. The component in this framework is available in all Talend Platform products with Big Data and in Talend Data Fabric. ## Basic settings | Properties | Description | | --- | --- | | 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.
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.

| | Edit Schema | Click Edit schema to make changes to
the schema. If you make changes, the schema automatically becomes built-in.

The output schema of tVerifyEmail has different read-only
columns depending on the options you select in the component Basic
settings view. Read-only output columns include:

VerificationLevel: provides you with the verification
status of the processed email addresses as the following:

-*VALID*: means that the email address comply with the defined
rule.

-*INVALID*: means that the email address does not comply with the
defined rule.

-*CORRECTED*: means that the input email does not comply with the
defined rule and has been corrected by using the content of the selected columns. This
column is available only when you select the Use column
content option in the LOCAL Part Options
section.

-*VERIFIED*: means that the email address does exist at the domain.
This column is available only when you select the Check with mail
server callback option.

-*REJECTED*: means that the email address does not exist at the domain.
This column is available only when you select the Check with mail
server callback option.

Suggested\_Email: provides you with a suggested content
for the email part before the @ sign. The email string is built up from the columns you
select from the Use column content view. | | Column to validate | Select from the list the column you want to validate with tVerifyEmail. | | Check the entire email with regular expression | Select this check box if you want to match the complete email address against a specific
regular expression.

Complete regular expression: enter the regular expression
against which you want to match email addresses.

This match is done as a first step to optimize the matching process and exclude addresses
that have problems before going any further to match the local and domain parts of email
addresses. | | LOCAL Part Options | Fields in this section will vary according to what option you select. "LOCAL part" in an
email address refers to the string before the @ sign.

-Use regular expression: enter in the Pattern field the expression against which you want to check the
local part of the email address.

-Use simplified pattern: enter in the
Pattern field the simplified pattern against which you
want to check the local part of the email address. Select the Show
syntax of simplified pattern option to display the syntax to use for simplified
patterns. For more information about the syntax, see [Simplified pattern syntax for tVerifyEmail](https://help.qlik.com/talend/r/en-US/8.0/components/email-validation/simplified-pattern-syntax-for-tverifyemail).

-Use column content: use the fields in this view to
decide the content against which you want to check the local part of the email. If the local
part does not match what you have defined, it will be rewritten by using the content of the
fields.

-Enable case-sensitive pattern matching: select this
check box to enable a case sensitive pattern matching of the local part of email addresses.
You can use case sensitive pattern matching with each of the above options. | | DOMAIN Part Options | Fields in this view will vary according to what option you select.

-Check the Top-level Domains and the following ones:
select this check box to verify the part of the email address which follows the last dot.
You can use the Additional Top-level Domains table to add
additional top-level domains against which you want to validate email addresses.

-Check domains with a black list: select this option to
verify the domains you define in the Domain list table as
black listed.

-Check domains with a white list: select this option to
verify the domains you define in the Domain List table as
white listed. | ## Global Variables | Variables | Description | | --- | --- | | Global Variables | ERROR\_MESSAGE: the error message generated by the
component when an error occurs. This is an After variable and it returns a string. This
variable functions only if the Die on error checkbox is
cleared, if the component has this checkbox.

A Flow variable functions during the execution of a component
while an After variable functions after the execution of the component.

To fill up a field or expression with a variable, press Ctrl+Space to access the variable list and choose the variable to use from
it.

For more information about variables, see [Using contexts and variables](https://help.qlik.com/talend/r/en-US/8.0/studio-user-guide/contexts-and-variables). | ## Usage | Usage guidance | Description | | --- | --- | | **Usage rule** | This component is used as an intermediate step.

This component, along with the Spark Batch component Palette it belongs to,
appears only when you are creating a Spark Batch Job.

Note that in this documentation, unless otherwise explicitly stated, a
scenario presents only Standard Jobs, that is to
say traditional
Talend
data integration Jobs. | | 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 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. |