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tarting in the 1980s, businesses recognized the need for keeping historical data and using it for analysis to assist in decision making It was soon apparent that data organized for use by day-to-day business transactions was not as useful for analysis In fact, storing significant amounts of history in an operational database (a database designed to support the day-to-day transactions of an organization) could have serious detrimental effects on performance William H (Bill) Inmon pioneered work in a concept known as data warehousing, in which historical data is periodically trimmed from the operational database and moved to a database specifically designed for analysis It was Inmon s dedicated promotion of the concept that earned him the title father of data warehousing The popularity of the data warehouse approach grew with each success story In addition to Inmon, others made significant contributions, notably Ralph Kimball, who developed specialized database architectures for data warehouses (covered in the Data Warehouse Architecture section, later in this chapter) EF (Ted) Codd added his endorsement to the data warehouse approach and coined two important terms in 1993: Online transaction processing (OLTP) Systems designed to handle high volumes of transactions that carry out the day-to-day activities of an organization Online analytical processing (OLAP) Analysis of data (often historical) to identify trends that assist in making strategic decisions regarding the business
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Up to this point, the chapters of this book have dealt almost exclusively with OLTP databases This chapter, on the other hand, is devoted exclusively to OLAP database concepts
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Using Inmon s definition, a data warehouse is a subject-oriented, integrated, time-variant, and nonvolatile collection of data intended to support management decision making Here are some important properties of data warehouses:
They are organized around major subject areas of an organization, such as sales, customers, suppliers, and products OLTP systems, on the other hand, are typically organized around major processes, such as payroll, order entry, billing, and so forth They are integrated from multiple operational (OLTP) data sources They are not updated in real time, but periodically, based on an established schedule Data is pulled from operational sources as often as needed, such as daily, weekly, monthly, and quarterly
The potential benefits of a well-constructed data warehouse are significant, including the following:
Competitive advantage Increased productivity of corporate decision makers Potential high return on investment as the organization finds the best ways to improve efficiency and/or profitability
However, there are significant challenges to creating an enterprise-wide data warehouse, including the following:
Underestimation of the resources required to load the data Hidden data integrity problems in the source data Omitting data, only to find out later that it is required Ever-increasing end user demands (each new feature spawning ideas for even more features) Consolidating data from disparate data sources High resource demands (huge amounts of storage; queries that process millions of rows) Ownership of the data Difficulty in determining what the business really wants or needs to analyze Big bang projects that seem never-ending
Databases: A Beginner s Guide
OLTP Systems Compared with Data Warehouse Systems
Data warehouse systems and OLTP systems are fundamentally different Here is a comparison: OLTP Systems
Hold current data Store current data Data is dynamic Database queries are short-running and access relatively few rows of data High transaction volume Repetitive processing; predictable usage pattern Transaction driven; support day-to-day operations Process oriented Serve a large number of concurrent users
Data Warehouse Systems
Hold historic data Store detailed data along with lightly and highly summarized data Data is static, except for periodic additions Database queries are long-running and access many rows of data Medium to low transaction volume Ad hoc and unstructured processing; unpredictable usage pattern Analysis driven; support strategic decision making Subject oriented Serve a relatively low number of managerial users (decision makers)
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