The following is an excerpt from Chapter 8 of Database Modeling and Design: Logical Design, Fourth Edition by Toby Teorey, published by Elsevier
A data warehouse is a large repository of historical data that can be integrated for decision support. The use of a data warehouse is markedly different from the use of operational systems. Operational systems contain the data required for the day-to-day operations of an organization. This operational data tends to change quickly and constantly. The table sizes in operational systems are kept manageably small by periodically purging old data. The data warehouse, by contrast, periodically receives historical data in batches, and grows over time. The vast size of data warehouses can run to hundreds of gigabytes, or even terabytes. The problem that drives data warehouse design is the need for quick results to queries posed against huge amounts of data. The contrasting aspects of data warehouses and operational systems result in a distinctive design approach for data warehousing.
A data warehouse contains a collection of tools for decision support associated with very large historical databases, which enables the end user to make quick and sound decisions. Data warehousing grew out of the technology for decision support systems (DSS) and executive information systems (EIS). DSSs are used to analyze data from commonly available databases with multiple sources, and to create reports. The report data is not time critical in the sense that a real-time system is, but it must be timely for decision making. EISs are like DSSs, but more powerful, easier to use, and more business specific. EISs were designed to provide an alternative to the classical online transaction processing (OLTP) systems common to most commercially available database systems. OLTP systems are often used to create common applications, including those with mission-critical deadlines or response times.
Let us now take a look at the core requirements and principles that guide the design of data warehouses (DWs) [Simon, 1995; Barquin and Edelstein, 1997; Chaudhuri and Dayal, 1997; Gray and Watson, 1998]:
DWs are organized around subject areas. Subject areas are analogous to the concept of functional areas, such as sales, project management, or employees, as discussed in the context of ER diagram clustering in Section 4.5. Each subject area has its own conceptual schema and can be represented using one or more entities in the ER data model or by one or more object classes in the object-oriented data model. Subject areas are typically independent of individual transactions involving data creation or manipulation. Metadata repositories are needed to describe source databases, DW objects, and ways of transforming data from the sources to the DW.
DWs should have some integration capability. A common data representation should be designed so that all the different individual representations can be mapped to it. This is particularly useful if the warehouse is implemented as a multidatabase or federated database.
The data is considered to be nonvolatile and should be mass loaded. Data extraction from current databases to the DW requires that a decision should be made whether to extract the data using standard relational database (RDB) techniques at the row or column level or specialized techniques for mass extraction. Data cleaning tools are required to maintain data quality—for example, to detect missing data, inconsistent data, homonyms, synonyms, and data with different units. Data migration, data scrubbing, and data auditing tools handle specialized problems in data cleaning and transformation. Such tools are similar to those used for conventional relational database schema (view) integration. Load utilities take cleaned data and load it into the DW, using batch processing techniques. Refresh techniques propagate updates on the source data to base data and derived data in the DW. The decision of when and how to refresh is made by the DW administrator and depends on user needs (e.g., OLAP needs) and existing traffic to the DW.
Data tends to exist at multiple levels of granularity. Most important, the data tends to be of a historical nature, with potentially high time variance. In general, however, granularity can vary according to many different dimensions, not only by time frame but also by geographic region, type of product manufactured or sold, type of store, and so on. The sheer size of the databases is a major problem in the design and implementation of DWs, especially for certain queries, updates, and sequential backups. This necessitates a critical decision between using a relational database (RDB) or a multidimensional database (MDD) for the implementation of a DW.
The DW should be flexible enough to meet changing requirements rapidly. Data definitions (schemas) must be broad enough to anticipate the addition of new types of data. For rapidly changing data retrieval requirements, the types of data and levels of granularity actually implemented must be chosen carefully.
The DW should have a capability for rewriting history, that is, allowing for "what-if" analysis. The DW should allow the administrator to update historical data temporarily for the purpose of "what-if" analysis. Once the analysis is completed, the data must be correctly rolled back. This condition assumes that the data are at the proper level of granularity in the first place.
A usable DW user interface should be selected. The leading choices today are SQL, multidimensional views of relational data, or a special-purpose user interface. The user interface language must have tools for retrieving, formatting, and analyzing data.
- Data should be either centralized or distributed physically. The DW should have the capability to handle distributed data over a network. This requirement will become more critical as the use of DWs grows and the sources of data expand.
Printed with permission from Morgan Kaufmann, a division of Elsevier. Copyright 2006. Database Modeling and Design: Logical Design, Fourth Edition by Toby Teorey. For more information about this title and other similar books, please visit elsevier.com.
This was first published in February 2006