can we select only particular columns in ADF Dataflow?

ONLINECAST

2 min read

·

05-10-2024

5

0

Share

Selecting Specific Columns in Azure Data Factory Data Flow: A Comprehensive Guide

Problem: You're working with large datasets in Azure Data Factory Data Flow and need to process only specific columns, leaving out others. This can be crucial for optimizing performance, reducing storage costs, and maintaining data privacy.

Solution: Fortunately, Data Flow provides flexible tools for selecting specific columns, allowing you to control the data you work with.

Understanding the Scenario:

Let's imagine you have a source dataset with the following schema:

CustomerID | CustomerName | CustomerEmail | CustomerPhone | OrderID | OrderDate | OrderTotal 
---------- | ------------ | ------------- | -------------- | -------- | -------- | ----------
12345     | John Doe     | [email protected] | 555-123-4567 | 1001    | 2023-03-15 | 100.00
67890     | Jane Smith   | [email protected] | 555-987-6543 | 1002    | 2023-03-18 | 50.00

You only need to work with the CustomerID, CustomerName, and OrderTotal columns for your current task.

Using Data Flow Transformations for Column Selection:

Data Flow provides two main ways to achieve this:

1. Derived Column Transformation:

   This transformation allows you to create new columns based on existing ones. You can use the derivedColumn function with the if statement to create a new column that contains a specific value (e.g., "selected") if the column you want to keep is present. Then, filter the data based on this new column.

   {
     "name": "SelectColumns",
     "type": "DerivedColumn",
     "description": "Select CustomerID, CustomerName, and OrderTotal columns",
     "inputs": [
       {
         "referenceName": "SourceData",
         "from": "Source"
       }
     ],
     "derivedColumns": [
       {
         "name": "IsSelected",
         "expression": "if(isNull(CustomerID) || isNull(CustomerName) || isNull(OrderTotal), 'not selected', 'selected')"
       }
     ]
   }
   

   After this step, you would need to use a filter transformation to select only the rows where IsSelected equals 'selected'.

2. Select Transformation:

   This transformation offers a more direct approach. It allows you to specify the columns you wish to keep and removes the rest.

   {
     "name": "SelectColumns",
     "type": "Select",
     "description": "Select CustomerID, CustomerName, and OrderTotal columns",
     "inputs": [
       {
         "referenceName": "SourceData",
         "from": "Source"
       }
     ],
     "select": [
       {
         "name": "CustomerID"
       },
       {
         "name": "CustomerName"
       },
       {
         "name": "OrderTotal"
       }
     ]
   }
   

   This transformation directly selects only the desired columns, eliminating the need for additional filtering.

Which Approach to Choose?

While both approaches achieve the same goal, the Select Transformation is generally recommended for its simplicity and efficiency. It directly selects the desired columns without requiring intermediate steps or complex expressions.

Additional Considerations:

• Performance Optimization: Selecting only the necessary columns reduces the data processed by subsequent transformations, leading to faster pipeline execution.
• Data Privacy: By selectively extracting columns, you can prevent sensitive information from being exposed in downstream processes.
• Storage Efficiency: Smaller datasets require less storage space and bandwidth for transfer, reducing costs.

Conclusion:

Selecting specific columns in Azure Data Factory Data Flow is a powerful feature for optimizing performance, controlling data access, and improving overall efficiency. Whether you use the Derived Column or Select transformation, you can confidently select the exact data you need for your processing tasks.