Validation Activity in Azure Data Factory

1. 32- Azure Data Factory
 Validation Activity in azure data factory
 Demo- Validation Activity
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2. Validation Activity:
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You can use a Validation in a pipeline to ensure the pipeline only continues execution once
it has validated the attached dataset reference exists, that it meets the specified criteria,
or timeout has been reached
In Azure Data Factory, the Validation activity is used to verify that the data in a source
data store meets certain criteria before it is processed further. This activity can help
ensure data quality and prevent errors downstream.
The Validation activity can be used to perform a wide range of data validation checks,
including:
1. Checking that the data confirms to a specific schema or data model.
2. Verifying that required columns or fields are present and contain valid data.
3. Checking for duplicate or missing data.
4. Ensuring that data falls within acceptable ranges or thresholds.
5. Validating that data meets certain business rules or constraints.

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To use the Validation activity, you need to define a validation rule using either a JSON
schema or an Azure Data Factory expression. The validation rule specifies the criteria that
the data must meet in order to pass validation.
Overall, the Validation activity is a useful tool for ensuring data quality and accuracy in
Azure Data Factory pipelines.
The "Validation" activity is an important tool for ensuring data quality and accuracy in your
Azure Data Factory pipelines, and can be used in conjunction with other activities such as
the "Copy" and "Transform" activities to create a comprehensive data processing workflow.

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