Forum Discussion
Reusable function for data transformation - user data functions
HI v-ssriganesh ,
As I explained in my previous response, based on Microsoft's response, the User data Functions cannot be used for transformations in the dataframe. Therefore, we need to utilize PySpark's native functions to transform data in the User data functions. So, I need to tweak my solution a bit and not use User data functions for Fabric but instead use pyspark.udf to do the transformation.
I think I know the way ahead now. Thanks for your support and help. We can close the ticket now.
Community SupportHello tinbaj,
Thank you for reaching out with your query.
To streamline your Julian date-to-standard date conversion across all eight notebooks, I recommend using Fabric User Data Functions (UDFs). You can create a single UDF in your Fabric workspace to define the conversion logic, which can then be called from all notebooks. This eliminates code duplication, simplifies maintenance, and ensures consistency across your JDE data sources. Simply create a UDF item, define the conversion function, and invoke it in each notebook. For more details, check the Fabric User Data Functions documentation: Overview - Fabric User data functions (preview) - Microsoft Fabric | Microsoft Learn.
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Thank you.