Characteristics of Data WarehousingA common way of introducing data warehousing is to refer to its fundamental characteristics (see Inmon, 2005):Subject oriented. Data are organized by detailed subject, such as sales, products, or customers, containing only information relevant for decision support. Subject orientation enables users to determine not only how their business is performing, but why. A data warehouse differs from an operational database in that most operational databases have a product orientation and are tuned to handle transactions that update the database. Subject orientation provides a more comprehensive view of the organization.Integrated. Integration is closely related to subject orientation. Data warehouses must place data from different sources into a consistent format. To do so, they must deal with naming conflicts and discrepancies among units of measure. A data warehouse is presumed to be totally integrated.Time variant (time series). A warehouse maintains historical data. The data do not necessarily provide current status (except in real-time systems). They detect trends, deviations, and long-term relationships for forecasting and comparisons, leading to decision making. Every data warehouse has a temporal quality. Time is the one important dimension that all data warehouses must support. Data for analysis from multiple sources contains multiple time points (e.g., daily, weekly, monthly views).Nonvolatile. After data are entered into a data warehouse, users cannot change or update the data. Obsolete data are discarded, and changes are recorded as new data.These characteristics enable data warehouses to be tuned almost exclusively for data access. Some additional characteristics may include the following:Web based. Data warehouses are typically designed to provide an efficient computing environment for Web-based applications.Relational/multidimensional. A data warehouse uses either a relational structure or a multidimensional structure. A recent survey on multidimensional structures can be found in Romero and Abelló (2009).Client/server. A data warehouse uses the client/server architecture to provide easy access for end users.Real time. Newer data warehouses provide real-time, or active, data-access and analysis capabilities (see Basu, 2003; and Bonde and Kuckuk, 2004).Include metadata. A data warehouse contains metadata (data about data) about how the data are organized and how to effectively use them.Whereas a data warehouse is a repository of data, data warehousing is literally the entire process (see Watson, 2002). Data warehousing is a discipline that results in applications that provide decision support capability, allows ready access to business information, and creates business insight. The three main types of data warehouses are data marts, operational data stores (ODS), and enterprise data warehouses (EDW). In addition to discussing these three types of warehouses next, we also discuss metadata.

1. Compare data integration and ETL. How are they related? 

2. What is a data warehouse, and what are its benefits? Why is Web accessibility important with a data warehouse?

3. Discuss the major drivers and benefits of data warehousing to end users. 

4. Describe how data integration can lead to higher levels of data quality. 

5. Investigate current data warehouse development implementation through offshoring.