Data Warehouse and Business Intelligence
There are many articles you can find regarding the differences between databases and data warehouses. While some consider them to be almost the same with very few differences others go on to provide in depth differences, pros and cons. Data Warehouses contain large amounts of data. Data can be made up of raw or formatted data. Its topics may include organization’s sales, salaries and operational data. It may also include human resource data, inventory data summaries of data including reports etc. Besides storing large amount of data, they must have systems in place that make it easy to use the data in day to day operations. Data warehouses are said to be a major factor in decision support systems. DSS is a technique used by businesses to derive facts, trends, or relationships that can help them make operational decisions and create effective strategies to achieve organizational goals.
Data warehouses and databases are different in many ways. The most prevalent difference is that the majority of databases put emphasis on a single application and that application will more than likely be concentrated on transactions for example subjects like payroll and inventories. The data analyzed will be done in one domain but it isn’t uncommon to have numerous domains.
Data warehouses deal with multiple areas and finds connections between the subjects in those areas. This will show how a company is doing as a whole and not only in one specific area as the database would. Another notable difference is that while a database is designed to record, a data warehouse is designed to respond to analysis questions that are essential to a business. In the article Data Warehouse a data warehouse is described as “A data warehouse is a place where data is stored for archival, analysis, and security purposes. Usually a data warehouse is either a single computer or many computers (servers) tied together to create one giant computer system.” (Data Warehouse article 2 background reading)
Data warehouse models include:
Online Transaction Processing: built for speed and easy use.
Online Analytical Processing: difficult to use, adds an extra step of analysis within the data and slows the process down requiring much more data in order to analyze certain queries.
Subject Oriented: data is linked together and is organized by relationships, any data changed in the data warehouse