A data warehouse is a database dedicated to storing information log term for the purposes of reporting and decision making. It is part of a multitiered scheme of databases to provide for a variety of needs. Data marts effectively small data warerhouses. The diffrence is data marts tend to be departmental and fairly application specific whereas data warehouses are more general.

  An example of this multiteered scheme might be a large corperation or organization like a bank. The bank would have diffrent databses for diffrent department. The investment banking dept for instance would have a variety of databases, and the checking/savings accounts departements would have a variety of databases, and the lending departments would have their own slew of databases. Each department of the company could consolidate all their databases into a larger databases dubbed a data mart. Using this data mart each departement can look for trends and index/argrigate data that might otherwise be seperated or disjointed. These data marts could provide a significant ammount of operational data for decision making. But we can then go a step further and create a bigger centralized database at the corperate level dubbed an Enterprise Data Warehouse where we consolidate all the data marts into a giant hodge podge of data where we can do even more reporting and aggrigations to find even broader trends and statistics in order to make even better decisions.

In this general case, you can see we're building a heirarchy. The departmental database servers (probly OLTP databases) are the ones doing the real day to day work of reading and writting data for operations. But the data marts and data warehouse(s) remain primarily read-only databases. All this ties in with analytical systems like OLAP (Online Analytical Processing) and other advanced Oracle extensions like Oracle Text (discussed in the next section) to provide a powerful centralized view of your entire organization. For instance, at the data warehouse we could run daily reports to determine what trends exist between the number of daily deposites into savings and investment accounts versus the number of new mortages. Or maybe we wanted to see if there is a corolation between online banking transactions and branch transactions based on geographical location using spatial data.

By building an intellegent hierarchy of databases we can ensure that advanced reporting (a CPU and disk intensive task) never interfears the transaction proccessing that they need to perform to keep business moving. It can ensure that there is never a need to overextend databases just to keep everything in one place even if it doesn't need to be for typical usage. And it can ensure that you have a wholistic view of your organizations data from multiple levels whether departmental or corperate.