Data Warehouse and Data Lakes

What data scientists might not know:
While learning to design dashboards and create models, data scientists are more familiar with it is based on data stored in data warehouses and sourced from data lakes. Data scientists might not know what the best techniques are to query data from the warehouse and what is the best way to look at that data holistically.

Key fundamentals-
The data warehouse is the centralized source of truth database created from multiple sources (each department might still have its own warehouse) (eg. Financial services industry data like credit card transactions)

Usually having a denormalized structure (for faster queries) and each table has been prepared and structured for a potential business case
Data Lakes are a step before data warehouses where raw data (including unstructured) is stored, all of the data is kept even if its purpose might not have been defined yet. (eg. Clinicians notes in healthcare)