Week 2 Assignment
Q: an explanation of how data warehousing, online transactional databases and data mining can solve or reduce data management difficulties
1. The term Data Warehouse was coined by Bill Inmon in 1990, which he defined in the following way: A data warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of data in support of management’s decision making process. He defined the terms in the sentence as follows:
Subject Oriented: Data gives information about a particular subject instead of about a company’s ongoing operations.
Integrated: Data gathered into the data warehouse from a variety of sources and merged into a coherent whole.
Time-variant: All data in the data warehouse is identified with a particular time period.
Non-volatile: Data is stable in a data warehouse. More data is added but data is never removed. This enables management to gain a consistent picture of the business. CS4221: Database Design 2 Data Warehouse (Source: “What is a Data Warehouse?” W.H. Inmon, Prism, Volume 1, Number 1, 1995).
A data warehouse system has the following characteristics:
• It provides a centralized utility of corporate data or information assets.
• It is contained in a well-managed environment.
• It has consistent and repeatable processes defined for loading operational data.
• It is built on an open and scalable architecture that will handle future expansion of data.
• It provides tools that allow its users to effectively process the data into information without a high degree of technical support.
Improved performance and availability Archiving and purging inactive data helps significantly improve query performance by reducing the amount of data and the number of indexes and table scans that must be processed. Smaller data warehouses also perform better with batch processing, long-running reports and ETL jobs—avoiding overruns into other production usage requests. Archiving makes performing periodic maintenance tasks easier and faster and it streamlines restoration from backups in the event of a failure for better system uptime and user productivity.
Managing data growth responsibly with data warehouse archiving Data warehouses should not be allowed to grow into large, expensive historical data repositories. Managing data growth with data warehouse archiving helps reduce costs, improve performance and increase availability for business-critical analytics and BI solutions while maintaining compliance with data retention requirements.What is Data Management?. (2016 Mar 22). Retrieved from https://studymoose.com/what-is-data-management-essay
In Data warehousing ,data is extracted from various sources as it is produced, making it easier and efficient for queries to be run over the data; it is then revolved into information for reporting for all levels of employees through the organization to understand, present and record(Lam,2012).
Data warehouse has help organization with minimizing it’s increased in data along with data being stored in different formats as well as data scattered across the organization, by allowing employees to have access to data at their fingertips, it has revolutionized the way organizations make decisions. (Mellanox, 2013);
2. Online transactional databases, utilize applications that manage changing data, performing real time data transactions, OLT databases has proven to be the most efficient way meet today’s organizational requirements such as high availability, high performance and scalability (Mellanox, 2013); Online transactional databases are used to assist with data reliability and relevancy, this process reduces the organization process execution time as well as storage cost.
On-Line Transaction Processing is a processing that supports the daily Business operations Also known as operational processing and OLTP. An OLTP is a database which must typically allow the real-time processing of SQL transactions to support traditional retail Processes e-commerce and other time-critical applications. It is also a Class of program that helps to manage or facilitate transaction oriented Applications such as data entry and retrieval transactions in a number of industries, including banking, airlines, mail order, supermarkets, and manufacturers. With today’s business environment, it is Impossible to run a business without having to rely on data. Processing online transactions these days increasingly requires Support for transactions spanning a large network or even the global internet and may include many companies.
1. Today, with the ubiquity of the internet, more and more people even from those remote areas are not doing transactions online through an e-commerce environment. The term transaction processing is often associated with the process wherein an online shop or ecommerce website accepts and processes payments through a customer’s credit or debit card in real time in return for purchased goods and services.
2. During the process of online transactions, a merchant payment system will automatically connect to the bank or credit card Company of the customer and carry out security, fraud and other checking for validity after which authorization to take the payment follows. In is strongly advised that when a company looks for other companies that will handle online transactions and processing, the company should have a system infrastructure that is robust, secure and reliable that give customers fast, seamless and secure checkout time.
3. An OLTP implementation tends to be very large involving very high volume of data at any given time. Business organizations have invested in sophisticated transaction management software like Customer Information Control System (CICS) and database optimization tactics that can help OLTP process very large numbers and volumes of concurrent updates on an OLTP oriented database.
4. There are also many OLTP brokering programs which can distribute transaction processing among multiple computers on a network that can enhance the functions of an OLTP working on a more demanding decentralized database system. Service oriented architectures and web services are now commonly integrated with OLTP.
The two main benefits with using OLTP are simplicity and efficiency. OLTP helps simplify a business operation by reducing paper trails and helping draw faster and more accurate forecasting for revenues and expenses. OLTP helps provide a concrete foundation with timely updating of corporate data. For an enterprise’ customers, OLTP allows the more choices on how they want to pay giving them more flexible time and enticing them to make more transactions. Most OLTP transactions offer services to customers 24 hours a day seven days a week.
What is OLTP? (n.d.). Retrieved from indstuds.yolasite.com/resources/
3. Data mining is the practice of encapsulating analyzed data from various perspectives into useful information also Data mining is predominantly used by organization and companies with a sturdy consumer focus, mainly retail, communication, marketing and financial organizations with data mining. Retailers are able to use the data to develop products and promotions to target specific customer segments (Lam, 2012). Data mining is made up of five major components: extract, alter and load transaction data on the data warehousing system manage and store the data in multi-dimensional database system. Allow business analysts and technology professionals to have access to data utilize application to analyze data. (Mellanox, 2013);
http://ecomputernotes.com/database-system/adv-database/data-warehouse by Dinesh Thakur
Chapter 19. Data Warehousing and Data Mining. (n.d.). Retrieved from