Data Ware House 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)

A simple architecture for a data warehouse, end users directly access data derived from several source systems through the data warehouse.

This illustrates three things:

• Data Sources (operational systems and flat files)
• Warehouse (metadata, summary data, and raw data)
• Users (analysis, reporting, and mining)

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)

In Architecture (basic), 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. In below image illustrates this typical architecture.

 

 

 

This illustrates four things:

• Data Sources (operational systems and flat files)
• Staging Area (where data sources go before the warehouse)
• Warehouse (metadata, summary data, and raw data)
• Users (analysis, reporting, and mining)

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

Although the architecture in second architecture 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. Below image 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.

 

 

This illustrates five things:

• Data Sources (operational systems and flat files)
• Staging Area (where data sources go before the warehouse)
• Warehouse (metadata, summary data, and raw data)
• Data Marts (purchasing, sales, and inventory)
• Users (analysis, reporting, and mining)