You might not know the workload of your data warehouse in advance, so a data warehouse should be optimized to perform well for a wide variety of possible query operations. Here are some examples of differences between typical data warehouses and OLTP systems:ĭata warehouses are designed to accommodate ad hoc queries. One major difference between the types of system is that data warehouses are not usually in third normal form (3NF), a type of data normalization common in OLTP environments.ĭata warehouses and OLTP systems have very different requirements.
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Figure 1-1 Contrasting OLTP and Data Warehousing Environments Contrasting OLTP and Data Warehousing Environmentsįigure 1-1 illustrates key differences between an OLTP system and a data warehouse. A data warehouse's focus on change over time is what is meant by the term time variant. This is very much in contrast to online transaction processing (OLTP) systems, where performance requirements demand that historical data be moved to an archive. In order to discover trends in business, analysts need large amounts of data. This is logical because the purpose of a warehouse is to enable you to analyze what has occurred. Nonvolatile means that, once entered into the warehouse, data should not change. When they achieve this, they are said to be integrated. They must resolve such problems as naming conflicts and inconsistencies among units of measure. Data warehouses must put data from disparate sources into a consistent format. Integration is closely related to subject orientation. Using this warehouse, you can answer questions like "Who was our best customer for this item last year?" This ability to define a data warehouse by subject matter, sales in this case, makes the data warehouse subject oriented. For example, to learn more about your company's sales data, you can build a warehouse that concentrates on sales. In addition to a relational database, a data warehouse environment includes an extraction, transportation, transformation, and loading (ETL) solution, an online analytical processing (OLAP) engine, client analysis tools, and other applications that manage the process of gathering data and delivering it to business users.Ĭhapter 10, "Overview of Extraction, Transformation, and Loading"Ī common way of introducing data warehousing is to refer to the characteristics of a data warehouse as set forth by William Inmon:ĭata warehouses are designed to help you analyze data. It separates analysis workload from transaction workload and enables an organization to consolidate data from several sources. It usually contains historical data derived from transaction data, but it can include data from other sources. Building the Data Warehouse by William Inmon (John Wiley and Sons, 1996)Ī data warehouse is a relational database that is designed for query and analysis rather than for transaction processing.The Data Warehouse Toolkit by Ralph Kimball (John Wiley and Sons, 1996).
This book focuses on Oracle-specific material and does not reproduce in detail material of a general nature.
Note that this book is meant as a supplement to standard texts about data warehousing. This chapter provides an overview of the Oracle data warehousing implementation.
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