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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 Internet 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 treats this data as a valuable asset to 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 two decades and continue to grow. Corporate IT surveys show that the majority of large corporations have some type of business analysis or data warehousing projects under way. 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 transaction-processing 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 in those of earlier years.
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Part VI:
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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 (given the name Online Analytical Processing (OLAP) database by E. F. Codd) 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 in the database workloads they produce. A typical online transaction processing of a customer s order might involve these database accesses: Read a row of the customer table to verify the proper customer number. Check the credit limit for that customer. Read a row of the inventory table to verify that a product is available. Insert a new row in an order table and in an order-items table to record the customer s order. Update the row of the inventory table to reflect the decreased quantity available.
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Database Characteristic Data contents Data structure Typical table size Access patterns Rows accessed per request Row coverage per access Access rate Access type Performance focus
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TABLE 21-1
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OLTP Database Current data Tables organized to align with transaction structure Thousands to a few million rows Predetermined for each type of transaction to be processed Tens Individual rows Many business transactions per second or minute Read, insert, and update Transaction throughput and speed
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Data Warehouse Database Historical data Tables organized to be easy to understand and query Millions to billions of rows Ad hoc, depending on the particular decision to be made Thousands to millions Groups (summary queries) Many minutes or hours per query Almost 100 percent read Query completion time
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