m scratch, leveraging these MARTs can reduce the development time and cost. According to Frederick Rook's prediction, (a Senior VP of Platinum Technology Inc.,) approximately 80% of the data warehouses or data marts for the vertical industries will be "pre-packaged" in the next two years or so. This approach definitely deviates from the traditional one. What Is a Star/Snowflake Schema? As mentioned earlier, the data warehouse database adopts a star or snowflake schema to maximize performance. A star or snowflake schema design is very different from that of an operational database schema design. In an operational database design, the data is highly normalized to support consistent updates and to maintain referential integrity. In a data warehouse design, the data is highly denormalized to provide instant access without having to perform a large number of joins. A star or snowflake schema design represents data as an array in which each dimension is a subject around which analysis is performed. As the name implies, the star schema is a modeling paradigm that has a single object in the middle radially connected to other surrounding objects like a star. The star schema mirrors the end user's view of a business query such as a sales fact that is qualified by one or more dimensions (e.g., product, store, time, region, etc.). The object in the center of the star is called the fact table. This fact table contains the basic business measurements and can consist of millions of rows. The objects surrounding the fact table (which appear as the points of the star) are called the dimension tables. These dimension tables contain business attributes that can be used as SQL search criteria, and they are relatively small. The star schema itself can be simple or complex. A simple star schema consists of one fact table and several dimension tables. A complex star schema can have more than one fact table and hundreds of dimension tables. Figure 2 depicts a ...
