Data warehousing is a vital tool for when data from multiple sources has been collected, to structure, compare and process data for improved business intelligence. Data warehousing stores both updated and historical data in one location. It can then be referred to for analytical reports, for business users and collaborative teams alike, for business decision support.
Data that has been stored in a data warehouse is transferred from operational systems or operational databases, for instance sales or marketing data. Data warehouse architecture is not a typical database. A standard database is created to manage a specific range of fast paced real time data expansion. A data warehouse offers a varied selection of data over an expansive period of time, including both current updating data and historical data.
The necessity for a data warehouse is apparent when analytical issues arise and requirements fail to be met regarding the performance of data analysis and operational databases or systems. Conducting such convoluted database queries is only possible when a database can enter a temporary fixed state, as is the case with data warehousing. A standard data lake is modified and updated consistently, which makes this option unfeasible.
A data warehouse can focus on data analysis to ensure a transactional database can concentrate solely on transactions. The additional advantages of using a data warehouse is the capacity to source and analyse data from multiple sources to consolidate variants in the storage schema.
However, data warehouses do have a downfall. Essentially a storage unit of data, data warehouses
do not have the ability to manage raw data.
Data preparation is thus a crucial element to building a data warehouse that is effectively operational.
