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Big Data Analytics

Data Warehouse Architectures

Data warehouses and their architectures vary depending upon the specifics of an
organization's situation. Three common architectures are:
■ Data Warehouse Architecture (Basic)
■ Data Warehouse Architecture (with a Staging Area)
■ Data Warehouse Architecture (with a Staging Area and Data Marts)

Data Warehouse Architecture (Basic)
Figure 1 shows a simple architecture for a data warehouse. End users directly access data derived from several source systems through the data warehouse.

In Figure 1, 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 such as August sales. A
summary in an Oracle database 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, as shown in following Figure 1. You can do this programmatically, although most data warehouses use a staging area instead. A staging area simplifies building
summaries and general warehouse management. Figure 2 illustrates this typical

Data Warehouse Architecture (with a Staging Area and Data Marts)
Although the architecture in Figure 2 is quite common, 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. Figure 3 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.

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