The data warehousing system is an essential part of the business intelligence and data management domain. It plays an indispensable role in collecting, storing, and managing massive amounts of data pulled from various sources. This helps in the querying of stored data and efficient data analysis. The following are the basic concepts of data warehousing framework outlined:

Central repository: It serves as a central repository to integrate into a single repository data from different sources to consolidate and store it. It facilitates the correlation of data from different systems into usable data presentations.

Data integration: It involves the process of extracting data from various sources data sources such as relational data handling systems, data from customer relations management, and Enterprise resource management along with various other sources, transforming it to the desired structure and compatible format and then loading it into data warehouses. This helps to correlate the data and standardized it into one single format.

Subject-oriented: operates around the notion of subject areas such as sales and customers as sales or customers as supplier and interactions Whenever a query is generated, the outcome is instantly around a particular subject.

Time-variant: It means that the data saved in a data warehouse is subservient to the particular point of time. This makes it tolerant to complex-subject questioning.

Non-volatile: It means that the data stored in the data warehouse is never edited or removed. Dimensional modeling . Fact table and dimension table central table to store factual data and the surrounding dimension table to increase the acceptability of data which makes the complex data queries.

Business Intelligence: data warehousing is used to create reports queries and dashboards. Scalability in terms of the data outburst.

High performance. Improve decision-making by empowering end-users with the ability to filter, sort and group data.