The data lake contains datasets in their original structure, raw data, with all warts and stuff, defined by the operational systems.

The data warehouse contains datasets in a unified (and usually simplified and reduced) form, defined by the needs of analysis tools.

If your analysis tool accesses the data lake directly, it will almost certainly contain "parsers" for various operational data formats. Also, it will perform those transformations over and over again every time. And multiple analysis scripts may contain multiple versions of those parsers. The idea is to separate these "parsers" out of the analysis step and to "cache" the cleaned-up intermediate result. That "cache of clean data" is usually called "data warehouse", and can create good indexes on that data, multiple runs of your analysis tools have very fast access.