Building Data Warehouse and Dashboard of Church Congregation Data

Authors

  • Ragil Yoga Irawan,  Universitas Kristen Duta Wacana
  • Budi Susanto,  Universitas Kristen Duta Wacana
  • Yuan Lukito,  Universitas Kristen Duta Wacana

(*) Corresponding Author

DOI:

https://doi.org/10.21460/jutei.2019.32.183

Abstract

A data warehouse is essential for an organization to process and analyze data coming from the organization. Hence, a data warehouse together with a dashboard to visualize the processed data are built to accommodate the need of the church administrator to analyze a large set of church congregation data. The data warehouse is built using the Kimball principle. This Kimball principle emphasizes the implementation of a dimensional model in the data warehouse, not a relational model used in a regular transactional database. An ETL process that contains extract, transform and load processes is used to retrieve all data from the regular transactional database and transform the data so the data can be loaded into the data warehouse. A dashboard is then built to visualize the data from the data warehouse so the users can view the processed data easily. Users can also export the processed data into an excel file that can be downloaded from the dashboard. A web service is built to get data from the data warehouse and return it to the dashboard.

References

“Gereja Kristen Indonesia Sinode Wilayah Jawa Tengah (GKI SW Jateng) - Deskripsi,” 2014. [Online]. Available: https://www.gkiswjateng.org/sinodes#deskripsi.

W. H. Inmon, Building the Data Warehouse, 4th ed. Indianapolis, IN: Wiley Publishing, Inc., 2005.

P. O’Donnell, D. Arnott, and M. Gibson, “Data Warehousing Development Methodologies: A Comparative Analysis,” in Decision Support in the Internet Age, 2002, pp. 387–398.

R. Kimball and M. Ross, The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd ed. Indianapolis, IN: John Wiley & Sons, Inc., 2013.

W. Höpken, M. Fuchs, G. Höll, D. Keil, and M. Lexhagen, “Multi-Dimensional Data Modelling for a Tourism Destination Data Warehouse,” in Information and Communication Technologies in Tourism 2013, 2013, pp. 157–169.

S. Mali, “Data Warehouse implementations to analyse Tennis Player performance in 2014,” 2016.

P. Vassiliadis, “A Survey of Extract-Transform-Load Technology.,” Int. J. Data Warehous. Min., vol. 5, no. 3, pp. 1–27, 2009.

J. Serra, “Building an Effective Data Wareheouse Architecture.” 2013.

Pivotal Software Inc., “About the Greenplum Architecture,” 2019. .

S. Few, “Dashboard Confusion Revisited,” Visual Business Intelligence Newsletter, 2007.

Pentaho Corporation, “Pentaho Data Integration,” 2017. .

W3C Working Group, “Web Services Glossary,” 2004. .

G. Alley, “Database vs Data Warehouse,” 2018. [Online]. Available: https://www.alooma.com/blog/database-vs-data-warehouse.

Downloads

Published

2021-07-13

How to Cite

[1]
R. Y. . Irawan, B. . Susanto, and Y. . Lukito, “Building Data Warehouse and Dashboard of Church Congregation Data”, JUTEI, vol. 3, no. 2, pp. 85–94, Jul. 2021.

Issue

Vol. 3 No. 2 (2019): Jurnal Terapan Teknologi Informasi

Section

Articles

License

Copyright (c) 2021 Ragil Yoga Irawan, Budi Susanto, Yuan Lukito

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Authors who publish articles in JUTEI agree on the following rules:
1. The author grants non exclusive royalty free rights, and is willing to publish articles online and complete (full access). With such rights JUTEI reserves the right to save, transfers, manages in various forms, maintains and publishes articles while keeping the author's name as the copyright owner.
2. Each author contained in the article has contributed fully to the substance and intellectual, and is accountable to the public. If in the future there is a copyright infringement notification then this will be responsibility of the author, not JUTEI.