In Azure Data Factory, you can use the Mapping Data Flows feature to parse XML data stored in an nvarchar column and map it to new columns.
- Create a new pipeline in Azure Data Factory.
- Add Copy Data Activity
- Set up the source dataset:
- Create a new dataset for your source data (e.g., SQL Server, Azure Blob Storage, etc.).
- Set the data type of the column containing the XML data as nvarchar.
- Import the schema or enter it manually, making sure the column containing the XML data is included.
- Set up the sink dataset:
- Create a new dataset for your destination data (e.g., SQL Server, Azure Blob Storage, etc.).
- Define the schema with the new columns "Catalog Name" and "RequireApprove" along with their respective data types (e.g., nvarchar for Catalog Name and bit for RequireApprove).
- Configure the mapping:
- In the "Copy Data" activity, click the "Mapping" tab.
- For each new column ("Catalog Name" and "RequireApprove"), click "Add dynamic content" and use the following expressions:
- Catalog Name:
@XPath(xml(columnName), 'string(/catalogs/Catalog/@Name)')
- RequireApprove:
@XPath(xml(columnName), 'string(/catalogs/Catalog/@RequireApprove)')
ReplacecolumnName
with the name of the column containing the XML data in your source dataset.
- Catalog Name:
- Configure the settings of the "Copy Data" activity as needed, such as concurrency, fault tolerance, etc.
- Publish the changes to your Azure Data Factory and trigger the pipeline to run. This will parse the XML data from the nvarchar column in the source dataset and map the Catalog Name and RequireApprove attributes to the corresponding columns in the sink dataset.