What is the difference between DWH and Data Lake?

Data Warehouse (DWH) and Data Lake are two different approaches to data storage and processing:

• Data Warehouse (DWH) — a structured data storage optimized for analytics and reporting. In DWH, data is usually cleaned, transformed, and organized into schemas (e.g., star or snowflake). This allows for fast execution of complex queries and report generation.

• Data Lake — a storage where data is kept in raw form, without prior processing. Data Lakes can store structured, semi-structured, and unstructured data (logs, images, videos, etc.). It is a flexible solution for storing large volumes of diverse data.

Main differences:

Characteristic	Data Warehouse	Data Lake
Data format	Structured, cleaned data	Raw, any formats
Purpose	Analytics, reporting	Storage, data preparation for ML and analytics
Storage cost	Usually higher due to processing	Usually cheaper, as it stores raw data

The choice depends on the task: for traditional business analytics, DWH is better; for flexible work with large and diverse data, Data Lake.