Business Intelligence and Data Warehouses

Topics: Data warehouse, Data management, Databases Pages: 2 (630 words) Published: October 26, 2014

Assignment 3: Business Intelligence and Data Warehouses
Instructor Name: Jan Felton
CIS 111
6/23/2014
Strayer University: Piscataway
Difference between the structure of database and warehouse transaction Database is designed to make transactional systems that run efficiently. Characteristically, this is type of database that is an online transaction processing database. An electronic strength record system is a big example of a submission that runs on an OLTP database. An OLTP database is typically controlled to a single application. The significant fact is that a transactional database does not lend itself to analytics. To effectively achievement analytics, you require a data warehouse. A data warehouse is a database of a diverse kind of an online analytical processing database (In Yang, In Everson & in Yin, 2004). A data warehouse survives as a layer on top of another OLTP databases. The data warehouse obtains the data from all these databases and builds a layer optimized for and dedicated to analytics. A database designed is used to handle transactions designed analytics. It is not structured to do analytics well. A data warehouse is structured to make analytics fast and easy. Operational data and decision support data

Operational and decision support data provide different purposes. Operational data are kept in a relational database that structures tables that tend to be extremely normalized. Operational data luggage compartment is optimized to support transactions that symbolize daily operations. For example, Customer data, and inventory data are in a frequent update mode. To provide effective modernize performance, operational systems keep data in many tables with the smallest number of fields. Operational data focus on individual transactions rather the effects of the transactions over time. In difference, data analysts tend to comprise of many data dimensions and are concerned in how the data recount over those dimensions Examples of...

References: Han, J., Kamber, M., & Pei, J. (2011). Data Mining: Concepts and Techniques. Burlington: Elsevier Science.
In Yang, Z. R., In Everson, R., & In Yin, H. (2004). Intelligent data engineering and automated learning - IDEAL 2004: 5th international conference, Exeter, UK, August 25-27, 2004 : proceedings. Berlin: Springer.
Principles of data mining. (2000). Cambridge, Mass: MIT Press.
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