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Figure 21-1 shows the architecture of a data warehousing environment. There are three key components: I Warehouse loading tools. Typically, a suite of programs that extract data from corporate transaction-processing systems (relational databases, mainframe and minicomputer files, legacy databases), process the data, and load it into the warehouse. This process typically involves substantial cleanup of the transaction data, filtering it, reformatting it, and loading it on a bulk basis into the warehouse. I A warehouse database. Typically, a relational database optimized for storing vast quantities of data, bulk loading data at high speeds, and supporting complex business analysis queries. I Data analysis tools. Typically, a suite of programs for performing statistical and time series analysis, doing what if analysis, and presenting the results in graphical form. Vendors in the data warehousing market have tended to concentrate in one of these component areas. Several vendors build product suites that focus on the warehouseloading process and challenges. A different group of vendors have focused on data analysis. There has been some vendor consolidation in each of these areas, but both remain areas of focus for individual independent software companies, including several whose revenues are in the $100 million range.

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/* Return total order amount for a customer */ create function get_tot_ords(c_num in number) return number as /* Declare one local variable to hold the total */ tot_ord number(16,2); begin /* Simple single-row query to get total */ select sum(amount) into tot_ord from orders where cust = c_num; /* return the retrieved value as fcn value */ return tot_ord; end;

21:

FIGURE 20-7

Figure 21-1.

20:

Specialized warehouse databases were also the target of several startup companies early in the data warehousing market. Over time, the major enterprise DBMS vendors also moved to address this area. Some developed their own specialized warehouse databases; others added warehouse databases to their product line by acquiring smaller companies that produced them. Today, the database component in the figure is almost always a specialized SQL-based warehouse DBMS supplied by one of the major enterprise database vendors.

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As the DBMS evaluates the search condition for each row of prospective query results, it uses the customer number of the current candidate row as an argument to the GET_TOT_ ORDS function and checks to see if it exceeds the $10,000 threshold. This same query could be expressed as a grouped query, with the ORDERS table also included in the FROM clause, and the results grouped by customer and salesperson. In many implementations, the DBMS carries out the grouped query more efficiently than the preceding one, which probably forces the DBMS to process the orders table once for each customer. Figure 20-8 shows the Informix SPL definition for the same stored function shown in Figure 20-7. Except for stylistic variations, it differs very little from the Oracle version. The Transact-SQL dialect used in Microsoft SQL Server and Sybase Adaptive Server Enterprise (ASE) has a stored (user-defined) function capability similar to the one illustrated in Figures 20-7 and 20-8.

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The initial focus of data warehousing was the creation of huge, enterprisewide collections of all of the enterprise s accumulated data. By creating such a warehouse of data, almost any possible question about historical business practices could be posed. Many companies started down the road to creating warehouses with this approach, but success rates were low. Large, enterprisewide warehouses generally proved too difficult to create, too big, and too unwieldy to use in practice. The focus eventually turned to smaller data warehouses focused on specific areas of a business that could most benefit from in-depth data analysis. The term data mart was coined to describe these smaller (but still often massive) data warehouses. With the advent of multiple data marts within enterprises, a recent area of focus has been on management of distributed data marts. In particular, there is a large potential for duplication of effort in the data cleansing and reformatting process when multiple marts are drawing data from the same production databases. The emerging answer

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