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As you can see the image of the post, the Data Warehouse is the larger part but it is mostly the back end with the ETL process, validations, data quality and aggregations for the transnational data. A data warehouse is a relational storage of meaningful information which is divided in to dimensions and facts to get better insights from it. It is a method of storing structured data (relational tables). This means the data is being used in a way that is validated against a set of rules which will help to get more answers to a variety of questions based on the subject area or the domain.
The traditional Data warehouse are used for end-to-end solution for business users from extracting data and aggregating it into special data models (dimensions and facts), the queries, all thru the applications/reports the users interacted with to consume that data.
BI is to describe the technologies to take this data from the data warehouse and present it to users as dashboards, reports, scorecards, and graphical analysis. It’s basically the data visualization part. Because, not everyone is tech savvy to query the databases which has millions of records using indexes and get the insights from rows and columns.
BI is alternatively also mentioned as Data Analytics. Yes, there is a thin line of difference but it’s almost the same when you look at getting the data from the Data Warehouse and do your analysis for the insights of it.
So, we can define a BI system as the solution for gathering data from multiple sources, transforming that data so that it is consistent and stored in a single location, and presenting its information to business users for analysis and decision-making.