Data Warehousing

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Data Warehousing, Data Marts and Data Mining
Data Marts
A data mart is a subset of an organizational data store, usually oriented to a specific purpose or major data subject, that may be distributed to support business needs. Data marts are analytical data stores designed to focus on specific business functions for a specific community within an organization. Data marts are often derived from subsets of data in a data warehouse, though in the bottom-up data warehouse design methodology the data warehouse is created from the union of organizational data marts. Data Mart Details

Data marts are the "corner stores" of the enterprise, and each unique knowledge worker community has its own mart maintained by the divisional or departmental IS group. Some divisions may need only a single data mart if all knowledge workers in the division have similar information requirements. In other cases, a departmental IS organization will discover several distinct knowledge worker communities within a single department of a division. Each data mart serves only its local community, and is modeled on the information needs of that community. 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 Data warehouse

Data warehouse is a repository of an organization's electronically stored data. Data warehouses are designed to facilitate reporting and analysis. This definition of the data warehouse focuses on data storage. However, the means to retrieve and analyze data, to extract, transform and load data, and to manage the data dictionary are also considered essential components of a data warehousing system. Many references to data warehousing use this broader context. Thus, an expanded definition for data warehousing includes business intelligence tools, tools to extract, transform, and load data into the repository, and tools to manage and retrieve metadata. In contrast to data warehouses are operational databases that support day-to-day transaction processing. Data Warehouses

Dramatic advances in data capture, processing power, data transmission, and storage capabilities are enabling organizations to integrate their various databases into data warehouses. Data warehousing is defined as a process of centralized data management and retrieval. Data warehousing, like data mining, is a relatively new term although the...
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