What is the difference between a data warehouse and a data mart?

Data Science, AI and ML
#1

A Data warehouse is a collection of data that has been separated from operating systems. This assists a company with their decision-making process. A data mart is a segment of a data warehouse dedicated to a certain business line. Data marts are repositories of condensed data gathered by an organisation for study on a certain subject or entity.

A data warehouse is often more than 100 GB, whereas a data mart is normally smaller than 100 GB. The architecture and utility of data marts are comparably easier due to the discrepancy in scope.

#2

Hello

#3

A data mart is a aggregated data set for a departmental solution pulled from 1 to 4 data sources.

A data warehouse is created by combining all departmental solutions into one data store. Often it is more aggregated.

A data lake typically contains large amounts of raw data that is not aggregated or structured.

When a data mart or data warehouse are designed the first question is usually “what questions do you want to ask of it”. The problem with this approach is that when you have new questions they sometimes cannot be answered. A data lake makes this possible but it might a day or so.