Data Warehouse Architectures

Data warehouses and their architectures vary depending upon the specifics of an
organization's situation. Three common architectures are:

1. Data Warehouse Architecture (Basic)
2. Data Warehouse Architecture (with a Staging Area)
3. Data Warehouse Architecture (with a Staging Area and Data Marts)

Data Warehouse Architecture (Basic)
In Figure, the metadata and raw data of a traditional OLTP system is present, as
is an additional type of data, summary data. Summaries are very valuable in data
warehouses because they pre-compute long operations in advance. For example, a
typical data warehouse query is to retrieve something like August sales. A
summary in Oracle is called a materialized view.

Data Warehouse Architecture (with a Staging Area)


You need to clean and process your operational data before putting it
into the warehouse. You can do this programmatically, although most data
warehouses use a staging area instead. A staging area simplifies building
summaries and general warehouse management.

Data Warehouse Architecture (with a Staging Area and Data Marts)

You may want to customize your warehouse’s architecture for different groups within your
organization. You can do this by adding data marts, which are systems designed for
a particular line of business. Above figure illustrates an example where purchasing,
sales, and inventories are separated. In this example, a financial analyst might want
to analyze historical data for purchases and sales.