This is always good to know how the technology or a particular tool has evolved over a period of time. This may not be necessary to have these details handy but I have experienced that knowing the history, many times, helps to learn the kind of problems, a particular technology has encountered and addressed. Let’s see, how Azure Data Factory (ADF) has evovled since its inception.
2015 was the year when ADF v1 arrvied in public preview
2016 ADF v1 was released as GA (General Availability) but needed lot of editing in JSON in order to get maximum out of it which was not that easy
2017 ADF v2 was launched in public preview
2018 ADF v2 was made available for production uses i.e. as General Availability (GA). This is when it started getting some traction and emerged as a data orchestration tool available on Azure for data integration projects. Data orchestration because it didn’t have any in-built capabilities for data transformation. In order to transform the data, you had to rely on other compute services outside ADF like SQL Server Stored Procedures, Azure HDInsight etc. And this is when SSIS lift and shift was added to ADF v2. This was something that many companies were waiting for to host their on-premise SSIS solutions on to Azure but without much rewriting of the code
2019 Data Flows availability made ADF a complete ETL/Data Integration tool i.e. transformation capabilities people have been waiting for were finally added to this