Designing ETL architectures involves choosing between the use of various forms of code-first, low-code, and no-code options for data processing. So what can ETL processes look like in Fabric when focusing on PySpark?

In this session, I'll showcase what ETL can look like in PySpark. I'll showcase libraries that can assist with the ETL process, as well as demonstrate how Fabric performs with different approaches to dataframe and Delta table usage. By the end of this session, you should understand how PySpark can help your ETL work, whether using it exclusively or with other Fabric features.