Employed to do the analytic work, leaving the transactional database free to focus on transactions.
Many types of business data are analyzed via data warehouses. The need for a data warehouse often becomes evident when analytic requirements run afoul of the ongoing performance of operational databases. Running a complex query on a database requires the database to enter a temporary fixed state. This is often untenable for transactional databases. A data warehouse is employed to do the analytic work, leaving the transactional database free to focus on transactions.
Data warehouses use a different design from standard operational databases. The latter are optimized to maintain strict accuracy of data in the moment by rapidly updating real-time data. Data warehouses, by contrast, are designed to give a long-range view of data over time. They trade off transaction volume and instead specialize in data aggregation.
Data warehousing is used to provide greater insight into the performance of a company by comparing data consolidated from multiple heterogeneous sources. A data warehouse is designed to run query and analysis on historical data derived from transactional sources.
OBusinesses might warehouse data for use in exploration and data mining, looking for patterns of information that will help them improve their business processes. A good data warehousing system can also make it easier for different departments within a company to access each other's data.
A data warehouse is not necessarily the same concept as a standard database. A database is a transactional system that is set to monitor and update real-time data in order to have only the most recent data available. A data warehouse is programmed to aggregate structured data over a period of time.
Three main types of Data Warehouses are:
