Introduction

One of the critical components in the information technology age is the data. Data is the source of all the information and information is valuable for decision making process.

Decision support systems are developed to support executive management and relevant decision makers. In the modern era, there is large volume of information is available. Data warehouse is required to store huge volume of data.

Since the data warehouse is supporting decision support system, therefore, it should be subject oriented, integrated, collected over a period of time and static.

Data Warehouse

Data warehouse has subject oriented data. This subject oriented data could be information such as sales, customer name, etc. Data warehouse excludes information, which is not useful for decision-making process.

Data warehouse is developed as an integration of multiple heterogeneous data sources. As the data source have their own data protocol, data processing is required while data warehousing.

Data warehouse provides information with time as function. This gives historical perspective to the information.

Once data is captured into the data warehouse, it cannot be changed.

Data within the data warehouse is maintained in form of star schema, snowflake schema and galaxy schema.

Data Mart

The data mart is that portion of the access layer of the data warehouse which is utilized by the end user. Therefore, data mart is a subset of the data warehouse. Data mart is usually assigned to a specific business unit within the enterprise. Data mart is used to slice data warehouse into a different business unit. Typically, ownership of the data mart is given to that particular business unit or department.

The primary utility of data mart is business intelligence. A data mart requires very less investment compared to data warehouse and therefore it is apt for smaller business. Set up time for data mart is very less again making it practical for smaller business.

The main advantages of data mart are as follows:

• It provides easy access to daily used data.
• It improves decision making process for end user.
• It is easy to create and maintain.

Online Analytical Processing (OLAP)

OLAP or Online Analytical Processing is a concept in which data is analyzed through multiple dimensions with help of structure called cube. OLAP helps in converting data into information.

The main objective of OLAP is to summarize information for decision making process from large data base. The report generated through OLAP can be presented in a format as per the requirement of end user.

The advantages of OLAP are as follows:

• It ensures that response to query is quicker consistently.
• It provides facility to work with data which are difficult to query through SQL.
• It lets user create view with the help of spreadsheet.

There are three types of OLAP multi-dimensional OLAP, relational OLAP and Hybrid OLAP. In multi-dimensional OLAP data is usually stored in proprietary structure suitable for multi-dimensional analysis. In relational OLAP data base is structure through standard database in star or snowflake schema. A combination of multi-dimensional OLAP and relational OLAP is the hybrid OLAP.