Data warehouse is an architecture for organizing information system. It is a process for building decision support systems and knowledge management environment that supports both day-to-day tactical decision making and long-term business strategies.

A data warehouse or enterprise data warehouse (DW, DWH, or EDW) is a relational database that is designed for query, reporting and data analysis rather than for transaction processing. It usually contains historical data derived from transaction data, but it can include data from other sources. It separates analysis workload from transaction workload and enables an organization to consolidate data from several sources. It is a central repository of data which is created by integrating data from one or more disparate sources. Data warehouses store current as well as historical data and are used for creating trending reports for senior management reporting such as annual and quarterly comparisons.

The data stored in the warehouse are uploaded from the operational systems (such as marketing, sales etc., shown in the figure to the right). The data may pass through an operational data store for additional operations before they are used in the DW for reporting.

A data warehouse maintains a copy of information from the source transaction systems. This architectural complexity provides the opportunity to:

  • Congregate data from multiple sources into a single database so a single query engine can be used to present data.
  • Mitigate the problem of database isolation level lock contention in transaction processing systems caused by attempts to run large, long running, analysis queries in transaction processing databases.
  • Maintain data history, even if the source transaction systems do not.
  • Integrate data from multiple source systems, enabling a central view across the enterprise. This benefit is always valuable, but particularly so when the organization has grown by merger.
  • Improve data quality, by providing consistent codes and descriptions, flagging or even fixing bad data.
  • Present the organization’s information consistently.
  • Provide a single common data model for all data of interest regardless of the data’s source.
  • Restructure the data so that it makes sense to the business users.
  • Restructure the data so that it delivers excellent query performance, even for complex analytic queries, without impacting the operational systems.
  • Add value to operational business applications, notably customer relationship management (CRM) systems.