Dynamic Data Masking with Azure Data Factory

Question

How can I mask specific columns when copying data from an on-premises source to Azure Data Lake Gen 2 using Azure Data Factory? Is it possible to use one data flow and a control table containing the table and column names to be masked for this purpose?

Answer 1

@Tushar Hatwar

Masking Data in Azure Data Factory Pipeline

There are two main approaches to achieve data masking for specific columns while copying data from on-premises to Azure Data Lake Gen 2 using Azure Data Factory (ADF):

1. Using a Data Flow Activity with Dynamic Configuration:

While ADF doesn't directly support dynamic masking within the copy activity, you can achieve it using a data flow activity:

• Data Flow Activity: Create a data flow activity within your ADF pipeline. This activity allows data transformation before loading it to the destination.
• Source Transformation: Define the source transformation to read data from your on-premises source.
• Data Flow Script: Here's where the magic happens:
• Use a scripting language like Python or .NET to read a control table containing the list of tables and columns to be masked.
• Dynamically generate masking logic based on the control table information. This could involve techniques like replacing sensitive data with asterisks (*) or using random characters.
• Apply the masking logic to the specific columns identified in the control table.
• Sink Transformation: Define the sink transformation to write the masked data to your Azure Data Lake Gen2 storage.

2. Leverage Azure Data Factory Integration with Azure Databricks:

• ADF Pipeline: Create a pipeline with a copy activity that reads data from your on-premises source.
• Data Transformation in Databricks: Configure the pipeline to trigger a Databricks notebook upon data arrival.
• Databricks Notebook: Within the notebook, read the control table and dynamically mask specific columns using libraries like Spark SQL.
• Write to Data Lake: Finally, the masked data is written to Azure Data Lake Gen2 storage.

Benefits of Control Table Approach:

• Centralized Configuration: Manage masking rules in a single control table, making it easier to maintain and update.
• Scalability: The control table approach can be easily extended to include new tables or columns for masking.

Choosing the Right Approach:

The best approach depends on your specific needs. The data flow activity with scripting offers a good balance of flexibility and control, while leveraging Databricks provides a more scalable solution for complex transformations.

Here are some additional resources that you might find helpful:

• PII detection and masking in ADF: https://learn.microsoft.com/en-us/azure/ai-services/language-service/personally-identifiable-information/overview
• Data Flow Activity in ADF: https://learn.microsoft.com/en-us/azure/data-factory/
• Azure Databricks with ADF: https://learn.microsoft.com/en-us/azure/data-factory/transform-data-using-databricks-notebook

Hope this helps. Do let us know if you any further queries.

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