A data warehouse (DW) contains corporate data and information gathered from numerous sources and systems. It will be created for supporting business decisions through the consolidation of data, analysis, and reporting at various aggregate levels.
Data will be added to the data warehouse via three processes: extraction, transformation, loading.
How Does a Data Warehouse Work?
Data warehouse architecture originated back in the ‘80s, as a model built for the support of data flow from operational systems to decision support systems.
Such systems require large quantities of heterogeneous data, gathered by companies over time, to be analyzed.
In data warehouses, data from numerous heterogeneous sources is extracted into one area. It will be transformed based on the needs of the decision support system, and stored in the warehouse. For instance, a business will store information related to its workers, their salaries, products built, client details, sales records, and invoices.
The company CEO may have a question they want to answer with regards to the current strategy for reducing costs, and the responses they receive will require all this data to be analyzed.
This is a core service of data warehouses: enabling executives to make well-informed decisions based on different raw data items.
As a result, a data warehouse can make a considerable contribution to decision making in the future. A business administration may query warehouse data to determine demand in the market for a specific product, compare regional sales data, or answer other key questions.
This will bring them insights into the steps required to market the product successfully. A data warehouse contains aggregate historical data, unlike an operational data store, which can be analyzed to make crucial decisions. The majority of large corporations utilize data warehouses, despite the money and work involved.