What is Data Mart ?

Data Mart is very similar to a Data Warehouse but limited in scope and purpose and is usually associated to one single domain. Often people get confused between Data Warehouse and Data Marts.
I have already written before about the Data Warehouse and need of Data Warehouse. If you have missed the same , you can read here : What is Data Warehouse ?
This article will give you an introduction to Data Mart, why it is needed  and different types.

What is Data Mart ?

Data Mart is a decentralized subset of data found either in Data Warehouse or as a standalone subset designed to support the unique business unit requirements of a specific decision support system. Data Marts have specific business related purposes such as measuring the impact of marketing promotions, or measuring and forecasting sales performance and so on.

A Data Mart is a subset of Data Warehouse.

Need Of Data Mart :

• End users will  get much better performance querying from a Data Mart than from a Data Warehouse.
• End users will have a much easier time navigating through Data Mart due to lesser amount of data.

Features Of Data Mart :

• Low Cost
• Controlled locally rather than centrally, conferring power on the user group.
• Contains less information than the warehouse
• Rapid response
• Easily understood and navigated than an enterprise Data Warehouse.
• Within the range of divisional and departmental budgets.

Types Of Data Mart :

1. Dependent  :- Allows to unite organization’s data in one data warehouse.
2. Independent  :- Created without the use of central data warehouse.
3. Hybrid  :- Allows to combine inputs from sources other than data warehouse.

Advantages of Data Mart :

• Typically single subject area and fewer dimensions.
• Limited feeds
• Very quick time to market (30-120 days to pilot)
• Quick impact on bottom line problems.
• Focused user needs.
• Limited scope.
• Optimum model for DW construction

Disadvantages of Data Mart :

• Does not provide integrated view of business information.
• Uncontrolled proliferation of data marts result in redundancy
• More number of data marts complex to maintain
• Scalability issues for large number of users and increased data volume.