Creating a data solution with Azure Data Factory (ADF) may look like a straightforward process: you have incoming datasets, business rules of how to connect and change them and a final destination environment to save this transformed data. Very often your data transformation may require more complex business logic that can only be developed externally (scripts, functions, web-services, databricks notebooks, etc.).
In this blog post, I will try to share my experience of using Azure Functions in my Data Factory workflows: my highs and lows of using them, my victories and struggles to make them work.
This includes a description of the options, a demo function, and additional notes for each technique.