The Hardware Platform

For adequate report and query performance, it is very important to have sufficient "horsepower" with the hardware platform. Scalability is of utmost importance.

Controlled Chaos

Do not despair if your computer environment looks like the one in Figure 2.1. This is more often the case than not in organizations of any size. What exists can at best be described as controlled chaos!

Figure 2.1. Controlled Hardware Chaos

Accompanying the hardware chaos are usually a huge portfolio of disparate software and a large staff with only enough skills to support the existing systems. In order to minimize the chaos, most organizations implementing a BI decision-support environment have to consider at least four imperatives in hardware platform selection.

1. New hardware platforms have to fit into the existing hardware configuration.

2. The DBMS on the selected hardware platform must perform well as database access and usage grow. Scalability is therefore one of the major issues to be addressed.

3. Platform selection is restricted by the need for interoperability between various hardware platforms (if required).

4. Cost and return on investment (ROI) for the previous three qualifiers are controlling factors.

Hardware Platform Requirements

The hardware must have sufficient power to handle complex access and analysis requirements against large volumes of data. It has to support not only predefined, simple queries on summary data but also ad hoc complex queries on detailed data. It must also be scalable because rapid changes will occur in:

• Data volumes

• Updating frequencies

• Data access patterns

• Number of reports and queries

• Number of people accessing the BI target databases

• Number of tools running against the BI target databases

• Number of operational systems feeding the BI target databases

It is useful to think of a BI decision-support environment in terms of a three- tier computing architecture (Figure 2.2). First, the extract/transform/load (ETL) engine extracts , cleanses, and transforms operational data. Then, using middleware, the BI target databases are populated . Finally, when data is requested , it is mapped into suitable representations for the business community at the interface level for running queries, reports, and online analytical processing (OLAP) applications. The interface level can be a customized graphical user interface (GUI) application, an enterprise portal, or Extensible Markup Language (XML) Web services.

Figure 2.2. Three-Tier Computing Architecture