Data Warehouse FeaturesThe key features of Data Warehouse such as Subject Oriented, Integrated, Nonvolatile and Time-Variant are are discussed below:
- Subject Oriented - The Data Warehouse is Subject Oriented because it provide us the information around a subject rather the organization's ongoing operations. These subjects can be product, customers, suppliers, sales, revenue etc. The data warehouse does not focus on the ongoing operations rather it focuses on modelling and analysis of data for decision making.
- Integrated - Data Warehouse is constructed by integration of data from heterogeneous sources such as relational databases, flat files etc. This integration enhance the effective analysis of data.
- Time-Variant - The Data in Data Warehouse is identified with a particular time period. The data in data warehouse provide information from historical point of view.
- Non Volatile - Non volatile means that the previous data is not removed when new data is added to it. The data warehouse is kept separate from the operational database therefore frequent changes in operational database is not reflected in data warehouse.
Data Warehouse ApplicationsAs discussed before Data Warehouse helps the business executives in organize, analyse and use their data for decision making. Data Warehouse serves as a soul part of a plan-execute-assess "closed-loop" feedback system for enterprise management. Data Warehouse is widely used in the following fields:
- financial services
- Banking Services
- Consumer goods
- Retail sectors.
- Controlled manufacturing
Data Warehouse TypesInformation processing, Analytical processing and Data Mining are the three types of data warehouse applications that are discussed below:
- Information processing - Data Warehouse allow us to process the information stored in it.The information can be processed by means of querying, basic statistical analysis, reporting using crosstabs, tables, charts, or graphs.
- Analytical Processing - Data Warehouse supports analytical processing of the information stored in it.The data can be analysed by means of basic OLAP operations,including slice-and-dice,drill down,drill up, and pivoting.
- Data Mining - Data Mining supports knowledge discovery by finding the hidden patterns and associations, constructing analytical models, performing classification and prediction.These mining results can be presented using the visualization tools.