How to do data transformation in your ETL process?
Working with raw or unprocessed data often leads to poor decision-making. This explains why data scientists, engineers, and other analytic professionals spend over 80% of their time finding, cleaning, and organizing data. Accordingly, the ETL process - the foundation of all data pipelines - devotes an entire section to T, transformations: the act of cleaning, molding, and reshaping data into a valuable format.