Data mart

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A data mart is the access layer of the data warehouse environment that is used to get data out to the users. The data mart is a subset of the data warehouse that is usually oriented to a specific business line or team. Data marts are small slices of the data warehouse. Whereas data warehouses have an enterprise-wide depth, the information in data marts pertains to a single department. 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 is a derogatory label describing 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.

The primary use for a data mart is business intelligence (BI) applications. BI is used to gather, store, access and analyze data. The data mart can be used by smaller businesses to utilize the data they have accumulated. A data mart can be less expensive than implementing a data warehouse, thus making it more practical for the small business. A data mart can also be set up in much less time than a data warehouse, being able to be set up in less than 90 days. Since most small businesses only have use for a small number of BI applications, the low cost and quick set up of the data mart makes it a suitable method for storing data.[2]

Design schemas[edit]

Reasons for creating a data mart[edit]

  • 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
  • Contains only business essential data and is less cluttered.

Dependent data mart[edit]

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.

The alternative school of data warehousing is that of Ralph Kimball. In his view, a data warehouse is nothing more than the union of all the data marts. This view helps to reduce costs and provides fast development, but can create an inconsistent data warehouse, especially in large organizations. Therefore, Kimball's approach is more suitable for small-to-medium corporations.[3]

See also[edit]

References[edit]

  1. ^ Data Mart Does Not Equal Data Warehouse
  2. ^ Case, Ruby (2012). Introduction to Information Systems. New Jersey: John Wiley & Sons, Inc. p. 128. ISBN 978-1-118-45213-4. 
  3. ^ Paulraj Ponniah. Data Warehousing Fundamentals for IT Professionals. Wiley, 2010, pp. 29–32. ISBN 0470462078.

Bibliography[edit]

External links[edit]