Source: http://en.wikipedia.org/wiki/Data_warehouse
In OLTP — online transaction processing systems relational database design use the discipline of data modeling and generally follow the Codd rules of data normalization in order to ensure absolute data integrity. In this approach, each of the more complex information items is resolved into a set of records in multiple tables, each of which satisfies the normalization rules. Codd defines 5 increasingly stringent rules of normalization and typically OLTP systems achieve a 3rd level normalization. Fully normalized OLTP database designs often result in having information from a business transaction stored in dozens to hundreds of tables. Relational database managers are efficient at managing the relationships between tables and result in very fast insert/update performance because only a little bit of data is affected in each relational transaction.
OLTP databases are efficient because they are typically only dealing with the information around a single transaction. In reporting and analysis, thousands to billions of transactions may need to be reassembled imposing a huge workload on the relational database. Given enough time the software can usually return the requested results, but because of the negative performance impact on the machine and all of its hosted applications, data warehousing professionals recommend that reporting databases be physically separated from the OLTP database.
In addition, data warehousing suggests that data be restructured and reformatted to facilitate query and analysis by novice users. OLTP databases are designed to provide good performance by rigidly defined applications built by programmers fluent in the constraints and conventions of the technology. Add in frequent enhancements, and too many a database is just a collection of cryptic names, seemingly unrelated and obscure structures that store data using incomprehensible coding schemes; all factors that while improving performance, complicate use by untrained people. Lastly, the data warehouse needs to support high volumes of data gathered over extended periods of time and are subject to complex queries and need to accommodate formats and definitions inherited from independently designed package and legacy systems.
Designing the data warehouse data Architecture synergy is the realm of Data Warehouse Architects. The goal of a data warehouse is to bring data together from a variety of existing databases to support management and reporting needs. The generally accepted principle is that data should be stored at its most elemental level because this provides for the most useful and flexible basis for use in reporting and information analysis.
Also this is on data mining,
Source: http://searchsqlserver.techtarget.com/s ... 01,00.html
Data mining is sorting through data to identify patterns and establish relationships.
Data mining parameters include:
* Association - looking for patterns where one event is connected to another event
* Sequence or path analysis - looking for patterns where one event leads to another later event
* Classification - looking for new patterns (May result in a change in the way the data is organized but that's ok)
* Clustering - finding and visually documenting groups of facts not previously known
* Forecasting - discovering patterns in data that can lead to reasonable predictions about the future (This area of data mining is known as predictive analytics.)
Now, flowing down to the real deal - Business Intelligence:
Source: http://searchdatamanagement.techtarget. ... 71,00.html
Business intelligence (BI) is a broad category of applications and technologies for gathering, storing, analyzing, and providing access to data to help enterprise users make better business decisions. BI applications include the activities of decision support systems, query and reporting, online analytical processing (OLAP), statistical analysis, forecasting, and data mining.
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