Reference no: EM13918416
Businesses today are extremely reliant on large amounts of data for making intelligent business decisions. Likewise, the data warehouses are often structured in a manner that optimizes processing large amounts of data.
Write a two to three (2-3) page paper in which you:
1. Outline the main differences between the structure of a relational database optimized for online transactions versus a data warehouse optimized for processing and summarizing large amounts of data.
2. Outline the main differences between database requirements for operational data and for decision support data.
3. Describe three (3) examples in which databases could be used to support decision making in a large organizational environment.
4. Describe three (3) examples in which data warehouses and data mining could be used to support data processing and trend analysis in large organizational environment.
5. Use at least three (3) quality resources in this assignment. Note: Wikipedia and similar Websites do not qualify as quality resources.
1. Outline the main differences between the structure of a relational database optimized for online transactions versus a data warehouse optimized for processing and summarizing large amounts of data.
Optimization
Optimized for performing read-write operations of single point transactions. An OLTP database should deliver sub-second response times. Performing large analytical queries on such a database is a bad practice, because it impacts the performance of the system for clinicians trying to use it for their day-to-day work. An analytical query could take several minutes to run, locking all clinicians out in the meantime.
Optimized for efficiently reading/retrieving large data sets and for aggregating data. Because it works with such large data sets, an OLAP database is heavy on CPU and disk bandwidth. A data warehouse is designed to handle large analytical queries. This eliminates the performance strain that analytics would place on a transactional system.
Data Organization
An OLTP database structure features very complex tables and joins because the data is normalized (it is structured in such a way that no data is duplicated). Making data relational in this way is what delivers storage and processing efficiencies-and allows those sub-second response times.
In an OLAP database structure, data is organized specifically to facilitate reporting and analysis, not for quick-hitting transactional needs. The data is de-normalized to enhance analytical query response times and provide ease of use for business users. Fewer tables and a simpler structure result in easier reporting and analysis.
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