The text discusses the evolution of ETL (Extract, Transform, Load) processes in the context of data processing. It highlights the shift from traditional ETL to ELT (Extract, Load, Transform), where data is first loaded into a database and then transformed using SQL or other tools. The author, Adrian Brudaru, argues that this approach is more accessible and powerful for transforming tables, columns, and rows. He introduces dlt, a Python-based library that automates the transformation process, making it easier to perform ETL or ELT tasks. The text also touches on the concept of EtLT (Extract, Transform, Load, Transform), which adds an additional step for complex scalar operations. Ultimately, the author concludes that while there are variations in how data is transformed, the fundamental process remains ETL with different flavors designating specific sub-types.