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 :
- Dependent :- Allows to unite organization’s data in one data warehouse.
- Independent :- Created without the use of central data warehouse.
- 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.