Add column mapping in Azure Data Factory

One of the key features of Azure Data Factory is the ability to add column mapping. Column mapping allows you to transform and manipulate data as it moves through your data pipelines. In this article, we will explore how to add column mapping in Azure Data Factory.

What is column mapping?

Column mapping is the process of defining how data should be transformed from the source to the destination. It involves mapping the columns from the source dataset to the columns in the destination dataset.

For example, let's say you have a source dataset with columns like FirstName, LastName, and Email. You want to load this data into a destination dataset that has columns like First_Name, Last_Name, and Email_Address. In this case, you would need to define a column mapping to map the source columns to the destination columns.

Adding column mapping in Azure Data Factory

To add column mapping in Azure Data Factory, follow these steps:

1. Open your Azure Data Factory instance and navigate to the pipeline where you want to add column mapping.
2. Click on the pipeline and then click on the Edit button.
3. In the pipeline editor, click on the activity where you want to add column mapping.
4. Click on the Mapping tab in the activity settings.
5. Click on the + New mapping button to create a new column mapping.
6. In the column mapping editor, you will see the source and destination datasets.
7. Click on the Add mapping button to add a new mapping.
8. From the source dataset, select the column you want to map.
9. From the destination dataset, select the corresponding column.
10. Click on the Save button to save the column mapping.

Repeat steps 7-10 for each column you want to map. You can also edit or delete existing mappings by clicking on the respective buttons.

Testing column mapping

Once you have added the column mapping, it's a good idea to test it to ensure that the data is being transformed correctly. Here's how you can test the column mapping:

1. Click on the Debug button in the pipeline editor to run a debug session.
2. Monitor the debug output to see if the data is being transformed as expected.
3. If you encounter any issues, you can go back to the column mapping editor and make the necessary changes.

It's important to note that column mapping is just one aspect of data transformation in Azure Data Factory. You can also perform other transformations like data type conversions, aggregations, and filtering.

Column mapping is a powerful feature in Azure Data Factory that allows you to transform and manipulate data as it moves through your data pipelines. By following the steps outlined in this article, you can easily add column mapping to your pipelines and ensure that your data is transformed correctly.

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