Transactional Database and Data Warehouse - Literature review Example

Running Head: TRANSACTIONAL DATABASE AND DATA WAREHOUSE Name Course Instructor Date Compare and contrast transactional databases and data warehouse With increased competition and technological advancements, organizations are adopting new information systems which largely provide them with the required information to achieve their desired goals. The relevant information obtained is gathered and stored in order to assists the organization decision makers to obtain quick as well as elaborated reports from the data. The data storage in today technological advanced globe has been successful due to the use of both transactional database and data warehouse. According to Trujil lo, et al (2001), a database is defined as an organized collection of data which provide more efficient retrieval. The collected information can either be printed, electronic, combinations, audio, graphical and statistical. On the other hand, a data warehouse is defined as an especially designed database which allow large amount of contextual as well as historical information to be stored and later accessed through analytical, multipart, aggregated but very efficient queries. According to Trujil lo, et al (2001), these queries’ are known to capture strategic information that is presented in the form of reports. Hwang (2007) maintain that, a data warehouse is designed to effectively enable business intelligence activities through effectively helping its users to enhance as well as enhance their company performance. The reports assist in supporting management in decision making. This paper aims to compare and contrast transactional databases and data warehouse. The paper will provide an over view of both transactional database and data warehouse giving benefits and demerits of each. From the above definitions of database and data warehouse, the main motivation behind the building of information systems was to effectively integrate information that will assist in supporting decision making (Trujil lo, et al 2001). With the beginning of transactional databases applications, the main aim of information system drifted towards effective organization of data in a manner that would allow the use of different software application. Trujil lo, et al (2001) maintains that, this drift in information system saw the specialization of transactional database to serve transactional information system. With increased development of information technology, there was need to establish data warehouse which saw the development of new design paradigms, adaptation of other data models. There are various similarities and differences of transactional database and data warehouse. Based on the differences, compared to the data warehouse, data stored in transactional data is considered to be the data required by the application. Here, data that is not to be by any other application is not stored. According to Golfarelli et al (1998), while data warehouse may include data historical data stored in transactional data do not include historical data. Hwang (2007) maintain that, while transactional database users are thousands of operators and administrators, most of the data warehouse users are analysts, managers and executives. Hwang (2007) maintain that, transactional databases users do not enjoy the optimization of data access they are required to balance the requirement of data access with the need to ensure integrity of data. On the other hand, data warehouse users require not only read access but also need this access to be very fast over a large volume of data (Golfarelli, Maio and Rizzi 1998). Based on the purpose, both the transactional databases and data warehouses differ significantly. Inmon (2002) maintain that, the transactional databases are involved in daily operations which give support to the different software applications. On the other hand, the main purpose of data warehouse is to ensure effective retrieval of information, generation of good report and effective data analysis (Inmon, 2002). Hwang (2007) maintain that, data warehouse is largely designed for analysis and query as compared to transactional database which is design for transactional processing. Data warehouse are to separate analysis workload from transaction workload thus enabling companies to merge data from various sources. Consolidation of data assist in effective data analysis thus gaining a better knowledge and understanding of the business and thus increased business performance (Inmon, 2002). Fig 1: Data warehouse architecture The other differences in both transactional database and data warehouse are on their data characteristics. Transactional database data is more about the organization of businesses inner working, effective changing of data internally (Golfarelli, Maio and Rizzi 1998). On the other hand, in data warehouse, it gives room for historical data, both internal and external data as well as descriptive data. There are three main characteristics of data warehouse; firstly, data here is summarized so as to ensure quick access and reporting. The other characteristic is data is efficiently integrated from various sources so as to ensure effective monitoring of business activities across an organization (Golfarelli, Maio and Rizzi 1998). The other characteristic is data is the efficient collection of operational data snapshot so as to facilitate trending and historical reporting as well as analysis. Clearly, it is quite evident that data warehouses are largely optimized for appropriate data access while transactional database are widely optimized for both access to integrity assurance measures and mechanism. Hwang (2007) maintain that, data warehouses places more emphasis on historical data so as to ensure they are in a position to support trend analysis as well as time series (Jukic 2006). While transactional database users are thousands of operators and administrators, most of the data warehouse users are analysts, managers and executives. Hwang (2007) maintain that, historical data captured by data warehouse ensures that organizations are in a position to discover the different hidden patterns, relationship in business operation and trends through its ability of analyzing large amounts of data. Transactional database put a demand that historical data is move to achieve to create more space for recent data (Jukic 2006). According to Jukic (2006), integration in information system is relatively related to subject orientation. In data warehouse, data need to be put from disparate sources into a more consistent manner. These is very important in resolving problem experiences in transactional data base such as naming conflicts as well as other inconsistencies that occur among units of measure. According to Jukic (2006), this integration further ensures the non volatility of data ware house as compare to that of the transactional database which is volatile. Non vitality here implies that once data is entered into the data warehouse is not change. This is logical in the sense that a user using data warehouse is able to analyze what has occurred as compared to that using transactional database. On comparing both the transactional database and data warehouse, it is evident as businesses collect increasingly data through using transactional databases, there is need to ensure the data is analyzed so as to make it more acute. Typically, the transactional database is designed to manage transactional processing and thus there is need to incorporate data warehouse to assists in analysis. According to Jukic (2006), the data warehouse ensure the data collected from the transactional process is understood while at the same time be able to generate ad hoc queries. Application of data warehouse is further segmented across various servers making it for its users to find the normalized tables. According to Jukic (2006), designing as well as building of a transactional database is critical part of establishing a successful data warehouse. Designing of the transactional database involve obtaining data from various sources and later combining it into a single and more logical model. Fig 2: Data warehouses integrate data from internal and external sources According to Jukic (2006), both transactional database and data warehouse can be considered as collection of data that is highly designed in support management that is useful in decision making. Further, these two methods contain some variety of data that present a more coherent picture of organizational conditions at a given time. Information in both transactional database and data warehouse is under the control of users and can be stored for some period of time safely. According to Jukic (2006), data warehouse work in conjunction with transactional databases and therefore assist in enhancing the value of operational business applications with main focus on customer relationship management systems. Transactional databases as well as data warehouse highly enable their users to gain deeper knowledge and understanding different aspects of their organizational data by way of consistent, fast and interactive access to vast possible views of data. This clearly allows users to view organizational data in a manner that is the best model of the real dimensionality of the business (Hwang 2007). In conclusion, it is quite evident from the differences between transactional databases and data warehouse , it is correct to say that transactional database are highly suitable for the traditional type of data storage technique in which the main importance is issued to the transactional processing. On the other hand, data warehouse is considered as a modern form of data storage which is very effective in processing huge volume of data and later retrieve important information from it. Further, it is quite evident that data warehouse is an emerging technology been adapted by businesses and organization across the globe since it is making OLAP (Online Analytical Processing) more possible. Data warehouse is making OLAP a real analytical processing procedure for volume of organizational data which in the past was considered not to be possible. Hwang (2007) maintain that, the use of separate data repositories known as data warehouse has and continues to improve the effectiveness and friendliness of reporting and other methodical tools. Not only are data warehouse are being used as platforms for good reporting within organization, they also make very perfect team with more exploring objective such as data mining as well as summarization. With today increased demands of powerful access tools capable of providing more analytical capability users are shifting from transactional databases to data warehouse. References Golfarelli, M., Maio, D., & Rizzi, S. 1998. Dimensional fact model, International Journal of Human-Computer Studies, 43(5,6), pp. 865-889. Hwang, H. 2007, The Effect of Implementation Factors on Data Warehousing Success: An Exploratory Study. Journal of Information, Information Technology, and Organizations, 2, pp. 1–14. Inmon, W. 2002. Building the Data Warehouse, 3rd Edition, Chichester: Jukic, N. 2006, Data Modeling Strategies and Alternatives for Data Warehousing Projects. Communications of the ACM, 49, 4, pp. 83–88. Trujil lo, J., Palomar, M., & Gómez, J. 2001. Designing data warehouses with OO conceptual models, IEEE Computer, 34(12). pp. 66-75. Read More