The increasing processing power and sophistication of analytical tools and techniques have resulted in the development of what are known as Data Warehouses.
These data warehouses provide storage, functionality, and responsiveness to queries beyond the capabilities of transaction-oriented databases.
Accompanying this ever-increasing power has come a great demand to improve the data access performance of databases.
Data warehouses provide access to data for complex analysis, knowledge discovery, and decision making. They support high-performance demands on an organization’s data and information. Several types of applications – OLAP DSS, and data mining applications – are supported.
OLAP (Online Analytical Processing)
OLAP is a term used to describe the analysis of complex data from the data warehouse. In the hands of skilled knowledge workers, OLAP tools use distributed computing capabilities for analyses that require more storage and processing power than can be economically and efficiently located on an individual desktop.
DSS (Decision-Support System)
DSS is also known as EIS (executive information systems) support an organization’s leading decision makers with higher level data for complex and important decisions.
Data mining is used for knowledge discovery, the process of searching data for unanticipated new knowledge.
Traditional databases support online transaction processing (OLTP), which includes insertions, updates, and deletions, while also supporting information query requirements.
Traditional relational databases are optimized to process queries that may touch a small part of the database and transactions that deal with insertions or updates of a few tuples per relation to process.
Thus, they cannot be optimized for OLAP DSS, or data mining. By contrast, data warehouses are designed precisely to support efficient extraction, processing, and presentation for analytic and decision making purposes.
In comparison to traditional databases, data warehouses generally contain very large amount of data from multiple sources that may include databases from different data models and sometimes files acquired from independent systems and platforms.