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## A loop starts with vbar_ set to 0, and increments it until ## the specified number of lines have been printed. vbar_=0 while [ $vbar_ -lt ${1:-$LINES} ] do printf "%${2:-1}.${2:-1}s$cu_NL" "$vb_char" vbar_=$(( $vbar_ + 1 )) done } ## A horizontal bar is a vertical bar one line deep. hbar() { vbar 1 ${1:-$COLUMNS} "$2" }

Imagine that you are a developer working for AdventureWorks (as in the SQL Server 2008 demo database). Someone from accounting stops at your office and asks for a breakdown of product sales. Therefore, you write a query in Management Studio, joining the Production.Product table to the Sales.SalesOrderDetail table, as shown in Figure 20-2.

A title sometimes looks better when centered on the screen or in a box. The calculations needed to do the positioning are not complicated, and are easily within the capabilities of the POSIX shell.

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Figure 20-2. Query and result: total sales by product With this task completed, you start your real work again, but then someone from manufacturing stops by and wants to see a sales breakdown by Production.DaysToManufacture, soon followed by a design team member who wants to see sales by Production.Color. Just as you re about to start writing these queries, the accounting guy returns, wanting to see a further breakdown of the product sales according to sales period (by month, quarter, and year) and also wanting you to include a breakdown by SalesPerson, Product, ProductCategory, ProductSubCategory, and Customer. Rather than writing custom query after custom query, the solution is to use Analysis Services. These examples of data interpretation are considered OLAP. An OLAP database is optimal for analysis and decision making. However, before analyzing and interpreting data, there must be data to analyze. This source data is often created, updated (and deleted when necessary) in a database environment known as an Online Transaction Processing (OLTP) system. For example, a retail establishment needs to record purchases, process returns, add new products, and receive new inventory. All this falls into the category of an OLTP system, and AdventureWorks is an example of a typical OLTP database. After the store closes, the IT of the AdventureWorks database will typically copy the data to a separate, structured, repository or a data warehouse. Business users can query the data warehouse to determine if denim jackets are profitable to sell and if plaid skirts sell more than striped shorts. One of the best sources of data for learning SQL Server 2008 is the AdventureWorksDW database, a sample database optimized for OLAP or data warehousing. You can download this at no cost from http://www.codeplex.com, where all the SQL Server code samples are now hosted.

Given their different characteristics, OLAP and OLTP environments require different configurations. Within an OLAP environment, it might make sense to have more indexes to optimize data for reading. In an OLTP system, each additional index can negatively affect the performance of insert, delete, and update operations. In order to make data simpler to interpret, an OLAP data system could have denormalized data. As we have all learned the hard way, fully normalized data can get extremely complex. In an OLAP environment optimized for ease of analysis, it is simpler to know an employee is part of the accounting department rather than knowing the employee belongs to DepartmentID=398. In addition, OLTP would favor normalization for performance and design clarity reasons. As we mentioned, when you have a separate repository for analysis-centric data, we typically refer to this as the data warehouse. Such repositories are often large (terabytes) and optimized for the delivery and analysis of copious amounts of information. OLAP does not require a data warehouse, but OLAP is often associated with data warehouses because OLAP is the technique for interpreting copious amounts of data. Integration Services facilitates populating the data warehouse. Analysis

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