A data warehouse (DW) is a database used for reporting. The data is uploaded from the operational systems for reporting. The data may pass through an operational data store for additional operations before it is used in the DW for reporting. A data warehouse maintains its functions in three layers: staging, integration, and access. Staging is used to store raw data for use by developers (analysis and support). The integration layer is used to integrate data and to have a level of abstraction from users. The access layer is for getting data out for users. This definition of the data warehouse focuses on data storage. The main source of the data is cleaned, transformed, catalogued and made available for use by managers and other business professionals for data mining, online analytical processing, market research and decision support (Marakas & OBrien 2009). However, the means to retrieve and analyze data, to extract, transform and load data, and to manage the data dictionary are also considered essential components of a data warehousing system. Many references to data warehousing use this broader context. Thus, an expanded definition for data warehousing includes business intelligence tools, tools to extract, transform and load data into the repository, and tools to manage and retrieve metadata. This book is your ultimate resource for Data Warehousing. Here you will find the most up-to-date information, analysis, background and everything you need to know. In easy to read chapters, with extensive references and links to get you to know all there is to know about Data Warehousing right away, covering: Data warehouse, Enterprise bus matrix, Aggregate (Data Warehouse), Anchor Modeling, Bitemporal Modeling, Bridgeware, Business analytics, Dashboard (business), Data extraction, Data mart, Data Vault Modeling, Data warehouse appliance, Data warehouse architectures, Degenerate dimension, Dimension (data warehouse), Dimension table, Dimensional Fact Model, Dimensional modeling, DIRKS, Document warehouse, Early-arriving fact, Extract, transform, load, Fact (data warehouse), Fact table, Bill Inmon, Integration warehouse, Ralph Kimball, Log trigger, Master data management, Measure (data warehouse), OLAP cube, Operational data store, Operational database, Operational system, Slowly changing dimension, Snowflake schema, Spreadmart, Staging (data), Star schema, The Kimball Lifecycle, Time variance This book explains in-depth the real drivers and workings of Data Warehousing. It reduces the risk of your technology, time and resources investment decisions by enabling you to compare your understanding of Data Warehousing with the objectivity of experienced professionals.