CDC Paramterizing Source and destination

Vivekanand Murugan 0 Reputation points
2023-07-16T06:58:02.8533333+00:00

How to implement CDC parametrizing the source and destination tables names in ADF (source and destination tables are SQL server only same versions).

Is Parametrization possible in CDC ADF.

Kindly let me know, if yes provide the solution how we can implement.

Regards,

Vivekanand Murugan

Windows 11
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  1. Limitless Technology 44,006 Reputation points
    2023-07-17T13:47:35.8533333+00:00

    Hello there,

    To implement Change Data Capture (CDC) in Azure Data Factory (ADF) with parameterized source and destination table names for SQL Server, you can follow these steps:

    Create the necessary source and destination tables in your SQL Server database.

    In Azure Data Factory, create a pipeline to perform the CDC operation. Here's a high-level overview of the steps you'll need:

    a. Create a pipeline with two parameters: SourceTableName and DestinationTableName.

    b. Add a Copy Activity to your pipeline.

    c. Configure the source dataset to point to the source table using the SourceTableName parameter. You can use the SQL Server connector to define the source dataset.

    d. Configure the destination dataset to point to the destination table using the DestinationTableName parameter. Again, use the SQL Server connector to define the destination dataset.

    e. In the Copy Activity, set the source and destination tables as follows:

    Source dataset table: @{pipeline().parameters.SourceTableName}

    Destination dataset table: @{pipeline().parameters.DestinationTableName}

    f. Configure any necessary mappings and transformations in the Copy Activity.

    g. Save and publish your pipeline.

    Once your pipeline is published, you can trigger it with different table names by passing the corresponding values for SourceTableName and DestinationTableName parameters. You can trigger the pipeline using triggers, manually, or via API calls, depending on your requirements.

    By parameterizing the table names in this way, you can reuse the same pipeline for different source and destination tables within the same SQL Server version. Remember to ensure that the schema and permissions are properly set for the tables and the user/service principal executing the ADF pipeline.

    I used AI provided by ChatGPT to formulate part of this response. I have verified that the information is accurate before sharing it with you.

    Hope this resolves your Query !!

    --If the reply is helpful, please Upvote and Accept it as an answer--

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