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ne of the most important applications of SQL and relational database technology today is the rapidly growing area of data warehousing and business intelligence. The focus of data warehousing is to use accumulated data to provide information and insights for decision making. With the rising popularity of the World Wide Web and the direct interaction with customers that it provides, the amount of data available about customer behavior (reflected in their click-by-click journey through web pages) has literally exploded. Data warehousing advocates that this data be treated as a valuable asset, and that it be translated, through analysis, into competitive advantage. The complementary process of data mining involves in-depth analysis of historical and trend data to find valuable insights about customer behavior and cross-dependencies. SQL-based relational databases are a key technology underlying data warehousing applications. Business intelligence applications have exploded in popularity over the last decade. Corporate IS surveys show that the majority of large corporations have some type of business analysis or data warehousing projects underway. In many ways, data warehousing represents relational databases coming full circle, back to their roots. When relational databases first appeared on the scene, the established databases (such as IBM s hierarchical IMS database) were squarely focused on business transactionprocessing applications. Relational technology gained popularity by focusing on decision support applications and their ad hoc queries. As the popularity of these applications grew, most relational database vendors shifted their focus to compete for new transaction-processing applications. With data warehousing, attention has turned back to what was formerly called decision support, albeit with new terminology and much more powerful tools than those of 15 years earlier.
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One of the foundations of data warehousing is the notion that databases for transaction processing and databases for business analysis serve very different needs. The core focus of an online transaction processing (OLTP) database is to support the basic day-to-day functions of an organization. In a manufacturing company, OLTP databases support the taking of customer orders, ordering of raw materials, management of inventory, billing of customers, and similar functions. Their heaviest users are the applications used by order-processing clerks, production workers, warehouse staff, and the like. By contrast, the core focus of a business intelligence (BI) database is to support business decision making through data analysis and reporting. Its heaviest users are typically product managers, production planners, and marketing professionals. Table 21-1 highlights the significant differences in OLTP and business intelligence application profiles and the database workloads they produce. A typical OLTP transaction processing a customer s order might involve these database accesses: I Read a row of the customer table to verify the proper customer number.
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I Read a row of the inventory table to verify that a product is available. I Insert a new row in an order table and an order-items table to record the customer s order. I Update the row of the inventory table to reflect the decreased quantity available. The workload presents a large volume of short, simple database requests that typically read, write, or update individual rows and then commit a transaction. The same type of workload is presented by all of the most frequent types of transactions, such as: I Retrieving the price of a product I Checking the quantity of product available I Deleting an order I Updating a customer address I Raising a customer s credit limit In contrast, a typical business analysis transaction (generating an order analysis report) might involve these database accesses: I Join information from the orders, order-items, products, and customers tables I Summarize the detail from the orders table by product in a summary query I Compute the total order quantities for each product I Sort the resulting information by customer This workload presents a single, long-running query that is read-intensive. It processes many rows of the database (in this case, every order item) and involves computing totals and averages and summarizing data. These characteristics are typical of almost all business analysis queries, such as: I Which regions had the best performance last quarter I How did sales by product last quarter compare to last year I What is the trend line for a particular product s sales I Which customers are buying the highest-growth products I Which characteristics do those customers share The difference between the business intelligence and the OLTP workloads is substantial and makes it difficult or impossible for a single DBMS to serve both types of applications.
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