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A data mart (DM) is a specialized version of a data warehouse (DW). Like data warehouses, data marts contain a snapshot of operational data that helps business people to strategize based on analyses of past trends and experiences. The key difference is that the creation of a data mart is predicated on a specific, predefined need for a certain grouping and configuration of select data. A data mart configuration emphasizes easy access to relevant information.

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 their data mart which includes 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.

Contents 1 Design Schemas 2 Reasons for Creating a Data Mart 3 Dependent Data Mart 4 External links

• 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
• starflake schema

• Eases 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

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 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.

• Data Mart vs Data Warehouse - Article comparing Data Mart vs Data Warehouse, and the ongoing methodology debate between the two titans of data warehousing.
• Database vs Data Warehouse - Difference between Data Mart/Data Warehouse and regular OLTP application databases.
• GNULAMP More on data mart..
• Creating Analytical Data Marts