Design of Data Warehouse for Minimarket’s Sales Information Using Tabular Models
Abstract
Alongside with development of technology, more business need support of business intelligence tools to transform its data in a meaningful way which is accumulated rapidly in every transaction occurred. In order to achieved that, data warehouse with its online analytical processing (olap) can be considered as a solution. One tools for creating an olap from data warehouse is SSAS. This tools consist of two models, first one is multidimensional models or can be said the traditional one and the new one is tabular models. One best feature that tabular models give is the time needed for building it. The tabular models does not require the data in format of fact and dimension tables so reduce the time to change particular data into some scheme (star or snowflake). Hence, the purpose of this research is to design a data warehouse for minimarket’s sales information using the advantage of tabular modeling for creating the analysis. Its also known that, the data analysis created using tabular, support reporting application such as pivot table excel, so the user not need to create a new application for reporting analysis
Downloads
References
[2] Rainardi, V. 2008. Building a Data Warehouse: With Examples in SQL Server. New York: Springer Verlag.
[3] Kimball, Ralph. 2004. The Data Warehouse ETL Toolkit : Practical Techniques for Extracting, Cleaning, Conforming, and Delivering Data. Indianapolis : Wiley Publishing, Inc.
[4] Partha, I., Weking, P. and Mertasana, P., 2019. Data Center Data Warehouse Development at Z Bali Clinic Using the Kimball Nine-Step Method. International Journal Of Engineering And Emerging Technology, 4(1), pp.53-59.
[5] Denny, Atmaja, I. P. M., Saptawijaya, a. And Aminah, S. 2017. Implementation of Change Data Capture in ETL Process for Data Warehouse Using HDFS and Apache Spark. 2017 International Workshop on Big Data and Information Security (IWBIS).pp. 49-55
[6] Bhaskara, I. M. A., Suardani, L. G. P. And Sudarma, M. 2018. Data Warehouse Implemantation To Support Batik Sales Information Using MOLAP. International Journal of Engineering and Emerging Technology, 3(1), pp. 45-51.
[7] Pramita, M. D. P., Subiksa, G. B. And Saputra, K. O. 2018. Design of Intermediate Medical Record Information System in Electricity Patient Using Rolap Warehouse Data. International Journal of Engineering and Emerging Technology, 3(1), pp. 56-61.
[8] Pradhana, I. G. N. A., Giriantari, I. A. D. And Sudarma, M. 2018. Analisis dan Perancangan Sistem Pengelola Data Menuju Implementasi Data Warehouse Untuk Mendukung Administrasi E-Procurement. Majalah Ilmiah Teknologi Elektro, 17(2), pp. 245-250.
[9] Iswardani, P., Pramana, I. and Saputra, K., 2019. Design of Data Warehouse for Monitoring Hotel’s Food and Beverage Cost. International Journal of Engineering and Emerging Technology, 4(1), pp.1-5.
[10] Docs.microsoft.com. 2020. Comparing Analysis Services Tabular And Multidimensional Models. [online] Available at:
[11] Wilson, D., 2017. Tabular Modeling With SQL Server 2016 Analysis Services Cookbook. Birmingham: Packt.
[12] Russo, M., Ferrari, A. and Webb, C., 2015. Microsoft SQL Server 2012 Analysis Services. United States of America: Microsoft.
[13] Sawant, N. and Shah, H., 2014. Big Data Application Architecture Q&A: A Problem - Solution Approach. New York: Apress.
[14] Narendra, A., Murpratiwi, S. and Sudarma, M., 2017. Design of E-Grant Application Data Warehouse. International Journal of Engineering and Emerging Technology, 2(1), pp.11-15.
[15] Kotama, I., Adnyana, A. and Saputra, K., 2019. Design of Data Warehouse for University Library using Kimball and Ross9Steps Methodology Case Study: Udayana University Postgraduate Library. International Journal of Engineering and Emerging Technology, 4(1), pp.37-40.
[16] Sumichan, A., Yudiana, I., Satrio, M. and Sudarma, I., 2019. Designing a Virtual Data Warehouse in Supporting Sales Information Needs (Case Study: National Scale Building Material Store X). International Journal of Engineering and Emerging Technology, 4(1), pp.45-48.
Indexed By
