Let’s be honest: the concept of a “data warehouse” isn’t anything particularly novel. Ingesting data from different sources across an organization as well as third parties, then transforming and storing it in a single location seems like the most logical way to make it easily accessible by users such as analysts and data scientists. How else would you be able to create data insights that are based on data that is generated from applications in different parts of the organization, say, for example, understanding the effectiveness of an advertising campaign by combining the marketing and sales data? Data warehousing, along with the often associated “ETL” (extract, transform, load) pattern, has long established itself as the standard approach to solving these types of problems.
This is a companion discussion topic for the original entry at https://greatexpectations.io/blog/ge-data-warehouse/