Azure Data Factory: How to Handle Null Values in Mapping Data Flows with SQL Server Source
I'm following best practices but I'm relatively new to this, so bear with me. I'm currently working on an Azure Data Factory pipeline that pulls data from a SQL Server database and processes it using Mapping Data Flows. I'm working with a scenario when it comes to handling null values in my source data. Specifically, I have a column in my SQL Server table that contains nulls, and when I try to map this column to another column in my data flow, it results in errors. I've tried using the 'coalesce' function to replace the nulls, like so: ```sql coalesce(column_name, 'default_value') ``` However, this doesn't seem to work as expected, and I still see errors in the Data Flow debug output, particularly `Data Flow behavior: Invalid input for column 'column_name'.`. I also attempted to set a default value in the Sink transformation, but that didn’t resolve the scenario either. I’m not sure if there’s a specific setting I need to enable or if there’s a different approach to handle nulls effectively in this context. Could anyone provide insights on how to correctly handle null values when mapping data flows in Azure Data Factory, or suggest alternative methods? I’m using Azure Data Factory version 2.0 and SQL Server 2019. Any help would be greatly appreciated! For context: I'm using Sql on Windows. Thanks in advance! I'm developing on Ubuntu 20.04 with Sql.