In: Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology, EDBT 2009, pp. 522–529, June 2014ĭayal, U., Castellanos, M., Simitsis, A., Wilkinson, K.: Data integration flows for business intelligence. In: 2014 IEEE International Congress on Big Data, pp. 1–10, October 2020īansal, S.K.: Towards a semantic extract-transform-load (ETL) framework for big data integration. In: 2020 IEEE Frontiers in Education Conference (FIE), pp. 1–7, October 2018Īqlan, F., Nwokeji, J.C., Shamsan, A.: Teaching an introductory data analytics course using microsoft access® and excel®. In: 2018 IEEE Frontiers in Education Conference (FIE), pp. In: Proceedings of the ACM (2011)Īqlan, F., Nwokeji, J.C.: Applying product manufacturing techniques to teach data analytics in industrial engineering: a project based learning experience. KeywordsĮl Akkaoui, Z., Zimànyi, E., Mazón, J.N.: A model-driven framework for ETL process development. Furthermore, we discuss the implications of our findings to ETL researchers and practitioners. (4) The prevailing challenges in developing ETL solutions. (3) The depth of coverage in ETL research and practice with regards to the application domains, frequency publications and geographical locations of papers. In this paper, we perform a systematic literature review to identify and analyze: (1) Approaches used to implement existing ETL solutions (2) Quality attributes to be considered while adopting any ETL approach. As the popularity and use of ETL grow, it becomes important to analyze and identify the trends in the research and practice of ETL. Over the decades, ETL has been applied to a wide range of domains such as finance, health and telecom to mention but a few. Although there are various approaches for data integration, Extract Transform and Load (ETL) has become one of the most efficient and popular approach. An important step in data analytics is data integration, during which historic data is gathered from various sources and integrated into a centralized repository called data warehouse. Data analytics plays a vital role in contemporary organizations, through analytics, organizations are able to derive knowledge and intelligence from data to support strategic decisions.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |