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. If you share the same pain points, if you find any mistakes or feel a total misrepresentation of facts, please leave your comments, there is no better opportunity to learn from positive critiques :-) Azure Functions gives you the freedom to create and execute a small or moderate size of code in C#, Java, JavaScript, Python, or PowerShell. This freedom releases you from a need to …