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Identify the Different Between Database and Data Warehouse

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Identify the Different Between Database and Data Warehouse Journal of Computer and Engineering Technology 5 (1) © Nabu Research Academy, 2018 Identify the Different Between Database and Data Warehouse Nur Rachman Dzakiyullah, Dep. of Information Technology, Universitas Aisyiyah Yogyakarta, [email protected], Karrar Abdulameer Albo Baqer , National Aerospace University Kharkiv Aviation Institute, [email protected]. Abstract— The database is designed, built, and maintained the information system. The dramatically increase in governments and companies’ transactions meet by increase in their databases, data storage and quires which used to retrieve data from database. They use information processing system which is used for storage of everyday activities about them. However, information processing systems rely on online transaction processing (OLTP) in database, which is not so easily to access to the governments and companies' users. Moreover, database was not designed to support multi-dimensional view. Therefore, Multi-dimensional view, Online Analytical Processing (OLAP) and reducing time consuming for reports generating leads to the concept of a data warehouse. This study review of database and data warehouse. Moreover, identify the different between them. Keywords-Database, data warehouse, OLTP, OLAP. I. Introduction Operational database is the database of records, consisting of system-specific background data and event data belonging to a system upgrade contract (Mohammed, Hasson, Shawkat and Al-khafaji, 2012; Sankaran, Suresh, Gupta, Nesamoney and Mukhopadhyay, 1998). It may also contain data monitoring system, such as indicators, flags, and counters. The operational database is the source of data for data warehouse (Jaber, Ghani, Suryana, Mohammed and Abbas, 2015; Khurram, S., 2008). It contains detailed data used to run the daily operations of the enterprise. These are constantly changing as we update and reflect the current value of the last transaction (Hasso, P., 2009). Operational database contains data entities that to date, and modifiable. In the system of enterprise data management, operational database can be seen as opposed to a colleague from the database, decision support, which contains non-modifiable data, which are for the purposes of statistical analysis (Mohammed, Ibrahim and Nadzir, 2016; Charles, J., and Grry, P., 2008). An example of a base for decision-making is that it provides data so that the average wages for different types of employees can be identified at that time as an operational database contains the same data that will be used to calculate the amount to pay for the testing of employees in Depending on the number of days that they have (Mohammed and Anad, 2014; Marotta, A., and Ruggia, R., 2002). The conceptual representation of database contents in different target has justified the needs for retrieving and generating the historical perspective of these targets. Thus, current data warehouses customized to be more flexible to generate the huge number of the incoming information in different data target. Recently, the field of DSS aims to justify the require trends rather without needs to look through the individual records in isolation. The founding was concerned on the importance of decision support queries in the Online Transaction Processing (OLTP) queries (Kang, 2002; Mohammed, Ibrahim, Shawkat and Hasson, 2013). The job of earlier on-line operational systems was to perform transaction and query processing. So, they are also termed as on-line transaction processing systems (OLTP). Data warehouse systems serve users or knowledge workers in the role of data analysis and decision-making. Such systems can organize and present data in various formats in order to accommodate the diverse needs of the different users. These systems are called on-line analytical processing (OLAP) systems. The data warehouse supports on-line analytical processing (OLAP), the functional and performance requirements of which are quite different from those of the on-line transaction processing (OLTP) applications traditionally supported by the operational databases. According to Inman (2003) data warehouse is “subject-oriented, integrated, time-varying, Non-Volatile data collection, which is used primarily in organizational decision-making process”. In other words, the data warehouse is not a change in the collection of data on a logical part of the business organization (Tim, 2004). These are usually quantitative result in some period of time such as day or month. To facilitate decision support, data storage has several different variations of the traditional system OLTP (Tim, 2004). There are many reasons to distinguish between data warehouse and traditional database. Data warehouse supports OLAP (Shi, H., et al., 2007). In addition, it is functional and performance requirements of which are quite different from those of the OLTP applications which are supported by the operational databases. OLTP applications usually computerizing clerical data processing tasks such as order entry and banking transactions, which from day to day operations of the organization (Shi, H., et al., 2007). These tasks are organized and repeated, and consist of short-term and isolated operations (Mendelzon, 2000). Operations require detailed, up to date data, as well as read or update multiple records access as a rule, primary keys. Operational databases are usually hundreds of megabytes to gigabytes in size. Sequence and the ability to restore databases are critical, and most of the entire operation is a key performance metric (Pasha, A., et al., 2004). To understand data warehousing further, the following four characteristics must be defined: Subject-oriented: Data warehouses are built around broad, non-overlapping subjects like customer, order, product, vendor, and time rather than around systems, functions, and processes like customer billing, order entry, and accounts payable. OLTP systems, on the other hand, are characterized by processes (Kimball, R. and Ross, M., 2002). Integrated: data is extracted from multiple, autonomous, Heterogeneous sources and is integrated by data wide consistencies in the measurement of variables, naming conventions, and physical data definitions (Chen, X., et al., 2008). Here the sources can be OLTP systems supporting different organizational needs. Such sources are mainly based on relational database systems like Oracle, Ingress, etc. It may also include mainframe computers having database applications written in COBOL or even flat files of data. It may include data from external sources like weather conditions or market conditions which do not necessarily form a part of the company database (Gary, P., and Greg, W., 2006). Time-variant: Data warehouse data is extracted from operational systems that enable it to be archived. This archival and subsequent historical value gives data warehouse an element of time as part of their structure. In fact, almost all the data warehouse applications have time as one of the dimensions (Paplpanas, T., 2000). Nonvolatile: Since the data in the data warehouse is a snapshot of corporation's data at a specific point in time, the data is relatively constant and doesn't change much with time (Rob, P., and Carlos, C., 2000). II. Data Warehouse Tools and Technique Data warehouses feature huge storage capacities that allow data collection without deletion or update options. Data warehouses also employ various tools and techniques. These tools can be used to clarify, structure, integrate, model, mine and multi-dimensional data. Upon the application of these tools, the data are ready to be used in DSS. Several tools and techniques are described below. Extract, Transform and Load (ETL): An ETL tool is used to extract clean data and information from multiple DBs. This tool then transforms retrieved data into a suitable form for data warehouses using special rules and tables. The suitable data are finally loaded onto a corresponding space in the data warehouse. Database Management System (DBMS): The DBMS systematically stores data inside warehouses following structures such as the star schema structure model and the snowflake schema structure. The star schema structure uses de-normalized dimensional tables, whereas the snowflake schema structure uses normalized dimensional tables. Such structures easily allow for a multi-dimensional view. They also facilitate easy access to data and quick response to queries (Inmon, 2003). Metadata: Metadata is used to structure information within data warehouses. Metadata provides descriptions and explanations for the flexible use and management of information resources. Metadata is also referred to as “data about data” because it provides elaborate descriptions of the data from the data itself. Data Mart: The data mart is a small repository of data and information that is built to store retrieved data according to their respective departments. The data mart is used to provide easy access to data and quick responses to queries. Moreover, the data mart can improve department performance (Kimball and Ross, 2002). OLAP, Data Mining and Online Analytical Mining (OLAM): OLAP (Online Analytical Process) is used to analyze data and information with multidimensional views of a cube. Thus, an OLAP cube provides a three-dimensional description of information. The DM tool is used to extract new knowledge from quantities of data. OLAM is a new tool that combines DM and OLAP to obtain information suitable for a DSS. Decision Support System (DSS): The DSS aids in the decision-making process
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