Data mart

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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. In practice, the terms data mart and data warehouse each tend to imply the presence of the other in some form. However, most writers using the term seem to agree that the design of a data mart tends to start from an analysis of user needs and that a data warehouse tends to start from an analysis of what data already exists and how it can be collected in such a way that the data can later be used. A data warehouse is a central aggregation of data (which can be distributed physically); a data mart is a data repository that may derive from a data warehouse or not and that emphasizes ease of access and usability for a particular designed purpose. In general, a data warehouse tends to be a strategic but somewhat unfinished concept; a data mart tends to be tactical and aimed at meeting an immediate need. One writer, Marc Demerest, suggests combining the ideas into a Universal Data Architecture (UDA). In practice, many products and companies offering data warehouse services also tend to offer data mart capabilities or services. 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.

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

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 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. as of now this is very impotant tool

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