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Key Points About Data Warehouse

Give a review on Data warehouse in a simple language by Dirk Herreman.

Data warehouse is a vital source for converting the data into the information form which further can be used for making intelligent decisions (Herreman, 2016). He provides a strong base for the various techniques of data analysis. It is a useful source for accessing the information in a quick and easy manner. The success of any business depends on achieving the goal, and this process is incomplete without taking corrective decisions. The author has explained some key points related to the data warehouse. The first key point is related to the ease for creating the database. The size of the data warehouse varies from business to business and from industry to industry, and it depends on the size and the requirement of the organization.  He has given guidance on choosing the right approach for establishing the data warehouse. Some organizations go for a huge database that is created in the centralized form. Some organizations create a small database, and some firms go for a combination of both types of databases a small as well as large database. It further depends on the data structure required by the firm.  The second key term is that the data warehouse is different from the operational databases. In a data warehouse, firstly the data structure is created, and then an application is created from it. He explained that it varies from the operational database as in an operational database, firstly the application is established, and then data is entered into the created application. The third main point is related to the creation of data structure in the data warehouse. He mentioned that this task is impossible to perform without using the data model. (Herreman, 2016). It is important to choose the accurate data model and correct data modeling technique for creating successful data warehouse so that it helps in taking corrective decision. There are various data modeling methods like dimensional data modeling, E-R modeling, etc. He said that the dimensional modeling was not suitable for handling bulky data models.

Conclusion and Recommendations: The author concludes that data warehouse provides the reliable information, and it provides the correct solutions for various issues. He concludes that it should be created by using the right method and right model (Herreman, 2016). He recommended various data analysis techniques, data models (like dimensional data modeling, E-R modeling), architecture, and he also guiding in choosing the right technique and right model depending on the size and requirement of the organization. The only drawback is that the reader required a complete knowledge of the Entity-Relationship model.

Conclusion and Recommendations

Critique and Critical analysis: This book is a very useful source for the beginners. This book provides the information on models, data structures, implementation, administration, development, and maintenance of the data warehouse. The author gives a clear picture to the readers related to the uses of the data warehouse. Before going for reading this book, the reader must have knowledge related to the Entity-Relationship modeling. The author has also explained the concept of data marts, and the way it varies from the data warehouse. He guides various recommendations on choosing the accurate data model, and right data structure for creating the data warehouse. The author says that its supports different data structures. The data can be accessed in the form of files, logs, and tables. The author elaborates that it does not behave well in case of data analysis for historical data. He explained the concept that the vast amount of data scanning was needed in case of accessing the historical data. It can cause impacts in a negative way on the operational application. The author has given suggestion also for handling this situation. He mentions that there should be a separate environment for handling the historical data as well as there should be a separate environment for handling the current data of the firm. It helps in reducing the conflicts between the information, and it also helps in increasing the performance of the overall system. In short, it is helpful in improving the operational environment of the company. The author says that the idea of warehouse starts form the concept of the relational database management system (Berghel, 2015). The author has also referred the reasons for creating this concept. He elaborates that it is created for handling the end user computing process. The quires are executed into the database by using SQL (Structured Query Language). He mentions that the concept of a data warehouse is generated to solve the following a query or issue: the requirement for the computing system of the end user is different form the requirements of the system used for the transactional processing. The author has explained the various techniques of data analysis in a easy way. The different tools are the query, and reporting tool, data mining tool, and the other is multi-dimensional analysis tool (Li, 2013). He provides the result corresponding to the queries made by the users. The result is given in the form of patterns, or the book can be given in the form of clustering attributes, and it further used for data analysis and then, the decision is made by the data analysis.

Critique and Critical Analysis

This book provides low level or beginner level material. I would suggest this book to the beginners, but it is not a fruitful source for advanced studies. This book is not a good for the industries working at large scale, but it is beneficial for the small scale companies, as it guides them in choosing the architecture for the creation of the data warehouse.

Readers view: This is an interesting book, and I have also created a copy of this book on my system. This book is a good source for beginning level users as earlier I don’t know about the concept of data warehouse, models, techniques, and architecture relate to it. After reading this book, I have understood the concept that how the data warehouse is a vital source in taking the accurate, and quick decisions. The author MS Raisinghani says about this article that there are various benefits of using this technology as application is created after the creation of data structure and it supports the population process in which the operational data, as well as the data from external systems, can be entered into the data warehouse (Raisinghani, 2016). It helps in event management by using the concept of the trigger. The trigger is executed whenever an event occurs in the system. It also maintains the log of changes and moreover, it provides the time stamp based capture which means it mentioned the time when the changes have been made in the system. I got an interesting concept that data warehouse is not a product, it is a solution. Before going through this book, I thought that it was a product or combination of different products. But, after going into the book, I realized that it is key to resolve various queries, and also the author says that it provides the capability for decision-making process (Doganaksoy, & Hahn, 2012). The author Steve Hoberman has also given positive feedback on this book, and he says (through this book) that the data warehouse helps in resolving conflictions; it helps in eliminating various issues like data redundancy, data consistency, etc. (Hoberman, 2016). I get clarification on the various aspects as the author elaborates the process for entering the data into the data warehouse; he has also mentioned the data maintained, and data structure methods (Herreman, 2016). He says that it provides the timely, understandable, accurate, and complete information. He mentioned that the only drawback of this technique is that it is costly, time- consuming, and inefficient method. He says that it is a costly process so it can’t be managed, and maintained by the small scale companies as they invest fewer amounts on maintaining the database as compare to the large scale companies.

References

Berghel, H. (2015). Simplified integration of Prolog with RDBMS. SIGMIS Database, 16(3), 3-12.

CACM Staff, (2011). How to celebrate Codd's RDBMS vision. Communications Of The ACM, 53(10), 7.

Doganaksoy, N. & Hahn, G. (2012). Data Mining: A Gateway to Better Data Gathering. Statistical Analysis Data Mining, 1(4), 280-283.

Gómez, L., Kuijpers, B., Moelans, B., & Vaisman, A. (2012). A Survey of Spatio-Temporal

Hameed Mousa, A., Shiratuddin, N., & Abu Bakar, M. (2014). Virtual Data Mart for Measuring Organizational Achievement Using Data Virtualization Technique (KPIVDM). Jurnal Teknologi, 68(3).

Herreman, D. (2016). Data modeling Techniques for Data Warehousing. IBM. Retrieved 13 June 2016,

Hoberman, S. (2016). Data modeling techniques explained: How to get the most from your data. Retrieved 15 June 2016

Li, d. (2013). RESEARCH ON DATA MART AND DATA MINING OF WELDING WORKSHOP. Chinese Journal Of Mechanical Engineering, 39(04), 79.

Marketos, G., Theodoridis, Y., & Kalogeras, I. (2011). Seismological Data Warehousing and Mining. International Journal Of Data Warehousing And Mining, 4(1), 1-16.

Naeem, M., Dobbie, G., & Weber, G. (2011). HYBRIDJOIN for Near-Real-Time Data Warehousing. International Journal Of Data Warehousing And Mining, 7(4), 21-42.

Raisinghani, M. (2016). Adapting Data Modeling Techniques for Data Warehouse Design. Retrieved 15 June 2016

Schuppert, A. & Perne, R. (2015). Data Mining mit Prozessdaten (Data Mining with Process Data). At - Automatisierungstechnik, 53(7/2005).

Thành, Đ. (2012). Macroeconomic data mart. JCC, 24(1).

Wegman, E. (2012). Special issue of statistical analysis and data mining. Statistical Analysis And Data Mining, 5(3), 177-177.

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