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Process fixed-length text files by using Data Factory mapping data flows

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APPLIES TO: Azure Data Factory Azure Synapse Analytics

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By using mapping data flows in Microsoft Azure Data Factory, you can transform data from fixed-width text files. In the following task, we'll define a dataset for a text file without a delimiter and then set up substring splits based on ordinal position.

Create a pipeline

  1. Select +New Pipeline to create a new pipeline.

  2. Add a data flow activity, which will be used for processing fixed-width files:

    Fixed Width Pipeline

  3. In the data flow activity, select New mapping data flow.

  4. Add a Source, Derived Column, Select, and Sink transformation:

    Fixed Width Data Flow

  5. Configure the Source transformation to use a new dataset, which will be of the Delimited Text type.

  6. Don't set any column delimiter or headers.

    Now we'll set field starting points and lengths for the contents of this file:

    1234567813572468
1234567813572468
1234567813572468
1234567813572468
1234567813572468
1234567813572468
1234567813572468
1234567813572468
1234567813572468
1234567813572468
1234567813572468
1234567813572468
1234567813572468

  7. On the Projection tab of your Source transformation, you should see a string column that's named Column_1.

  8. In the Derived column, create a new column.

  9. We'll give the columns simple names like col1.

  10. In the expression builder, type the following:

    substring(Column_1,1,4)

    derived column

  11. Repeat step 10 for all the columns you need to parse.

  12. Select the Inspect tab to see the new columns that will be generated:

    inspect

  13. Use the Select transform to remove any of the columns that you don't need for transformation:

    select transformation

  14. Use Sink to output the data to a folder:

    fixed width sink

    Here's what the output looks like:

    fixed width output

The fixed-width data is now split, with four characters each and assigned to Col1, Col2, Col3, Col4, and so on. Based on the preceding example, the data is split into four columns.

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  • Build the rest of your data flow logic by using mapping data flows transformations.