Data mart

A data mart (DM) is the access layer of the data warehouse (DW) environment that is used to get data out to the users. The DM is a subset of the DW, usually oriented to a specific business line or team.

Terminology

There can be multiple data marts inside a single corporation; each one relevant to one or more business units for which it was designed. DMs may or may not be dependent or related to other data marts in a single corporation. If the data marts are designed using conformed facts and dimensions, then they will be related. In some deployments, each department or business unit is considered the owner of its data mart including all the hardware, software and data.^[1] This enables each department to use, manipulate and develop their data any way they see fit; without altering information inside other data marts or the data warehouse. In other deployments where conformed dimensions are used, this business unit ownership will not hold true for shared dimensions like customer, product, etc.

The related term spreadmart describes the situation that occurs when one or more business analysts develop a system of linked spreadsheets to perform a business analysis, then grow it to a size and degree of complexity that makes it nearly impossible to maintain.

Design schemas

• Star schema or dimensional model is a fairly popular design choice, as it enables a relational database to emulate the analytical functionality of a multidimensional database.
• Snowflake schema
• Datamart Architecture Pattern (EA Reference Architecture)

Reasons for creating a data mart

• Easy access to frequently needed data
• Creates collective view by a group of users
• Improves end-user response time
• Ease of creation
• Lower cost than implementing a full data warehouse
• Potential users are more clearly defined than in a full data warehouse

Dependent data mart

According to the Inmon school of data warehousing, a dependent data mart is a logical subset (view) or a physical subset (extract) of a larger data warehouse, isolated for one of the following reasons:

• A need refreshment for a special data model or schema: e.g., to restructure for OLAP
• Performance: to offload the data mart to a separate computer for greater efficiency or to obviate the need to manage that workload on the centralized data warehouse.
• Security: to separate an authorized data subset selectively
• Expediency: to bypass the data governance and authorizations required to incorporate a new application on the Enterprise Data Warehouse
• Proving Ground: to demonstrate the viability and ROI (return on investment) potential of an application prior to migrating it to the Enterprise Data Warehouse
• Politics: a coping strategy for IT (Information Technology) in situations where a user group has more influence than funding or is not a good citizen on the centralized data warehouse.
• Politics: a coping strategy for consumers of data in situations where a data warehouse team is unable to create a usable data warehouse.

According to the Inmon school of data warehousing, tradeoffs inherent with data marts include limited scalability, duplication of data, data inconsistency with other silos of information, and inability to leverage enterprise sources of data.

See also

• Data warehouse
• Enterprise architecture
• OLAP cube

References

1. ^ Data Mart Does Not Equal Data Warehouse

Bibliography

• Inmon, William (2000-07-18). "Data Mart Does Not Equal Data Warehouse". DMReview.com. http://csis.bits-pilani.ac.in/faculty/goel/Data%20Warehousing/Articles/Data%20Marts/dataWarehouse_com%20%20Article_DM%20VS%20DW.htm. 

External links

• Creating Analytical Data Marts