While data lakes and ETL and both used in the context of data management and analytics, they have different use cases. ETL, or Extract, Transform, and Load, involves the extraction and aggregation of structured data from multiple sources, and then loading it into a data warehouse. ETL is traditionally a ‘big data’ concept. Data lakes, on the other hand, can be read directly for data analytics and data science applications. The customer always has control of the data, which significantly simplifies data architecture.