Data Warehouse Characteristics
There are several characteristics from Data Warehouse, as follows:
- Subject Oriented
Subject oriented data warehouse means data warehouse designed to analyze data based on subject-specific subject within the organization, rather than on the particular application or function. The data warehouse is organized around the main subjects of the company (customers, products and sales) and not organized in areas of major applications (customer invoicing, stock control and product sales). This is due to the need of a data warehouse for storing data that is as supporting a decision, rather than the data-oriented applications.
Data Warehouse can store data coming from separate sources into a format that is consistent and integrated with each other. Thus, the data can not be broken because the data is an entity that supports the overall concept of the data warehouse itself.
Terms of the integration of data sources can be met in various ways crate consistent in naming variables, consistent variable in size, consistent in coding structure and consistency in the physical attributes of the data. Examples of the operational environment there are various applications that may also be made by a different developer.
Therefore, it is possible in these applications there is a variable that has the same intent but different name and its format. These variables must be converted to the same name and format agreed. Thus there is no more confusion because of different names, formats, and so forth. Then the data can be categorized as an integrated data.
- Time Variant
All data in the data warehouse can be said to be accurate or valid at any given time. To view the time interval used to measure the accuracy of a data warehouse, we can use the way include:
- The simplest way is to present the data warehouse at a certain time range, such as between 5 to 10 years into the future.
- The second way, using variations / differences in time are included in data warehouse either implicitly or explicitly, an explicit the element of time in days, weeks, months and so on. Implicitly for example, when the data is duplicated at each end of the month, or quarterly. The element of time will remain implicit in the data.
- The third way, the time variation presented data warehouse through a long series of snapshots. Snapshot is a partial view of the specific data corresponding user desires of all the data that is read-only.
The fourth characteristic of the data warehouse is non-volatile, meaning that the data in the data warehouse is not updated in real time but in the refresh of the operating system on a regular basis. The new data are being added as a supplement to the database itself rather than as a change. The database is continuously absorb new data, then incremental together with previous data.
Unlike the operational database to perform the update, insert and delete the data that change the contents of the database while the data warehouse there are only two events manipulate data that is loading the data (fetch data) and data access (access data warehouse as do a query or display a report required, no activity of updating the data).