Big data etl implementation approaches: A systematic literature review

Joshua C. Nwokeji, Faisal Aqlan, Anugu Apoorva, Ayodele Olagunju

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Extract, transform, load (ETL) is an essential technique for integrating data from multiple sources into a data warehouse. ETL is applicable to data warehousing, big data, and business intelligence. Through a systematic literature review of 97 papers, this research identifies and evaluates the current approaches used to implement existing ETL solutions. We found that conceptual modeling such as UML, BPMN, and MDA is the most popular approach used to implement ETL solutions. However, innovative approaches such as machine learning, artificial intelligence, and robotics are either under-utilized or not used at all to develop ETL solutions. Additionally, we discuss the implications of these to ETL research and practice.

Original languageEnglish (US)
Title of host publicationProceedings - SEKE 2018
Subtitle of host publication30th International Conference on Software Engineering and Knowledge Engineering
PublisherKnowledge Systems Institute Graduate School
Pages714-715
Number of pages2
ISBN (Electronic)1891706446
DOIs
StatePublished - Jan 1 2018
Event30th International Conference on Software Engineering and Knowledge Engineering, SEKE 2018 - Redwood City, United States
Duration: Jul 1 2018Jul 3 2018

Publication series

NameProceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE
Volume2018-July
ISSN (Print)2325-9000
ISSN (Electronic)2325-9086

Other

Other30th International Conference on Software Engineering and Knowledge Engineering, SEKE 2018
CountryUnited States
CityRedwood City
Period7/1/187/3/18

Fingerprint

Data warehouses
Competitive intelligence
Artificial intelligence
Learning systems
Robotics
Big data

All Science Journal Classification (ASJC) codes

  • Software

Cite this

Nwokeji, J. C., Aqlan, F., Apoorva, A., & Olagunju, A. (2018). Big data etl implementation approaches: A systematic literature review. In Proceedings - SEKE 2018: 30th International Conference on Software Engineering and Knowledge Engineering (pp. 714-715). (Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE; Vol. 2018-July). Knowledge Systems Institute Graduate School. https://doi.org/10.18293/SEKE2018-152
Nwokeji, Joshua C. ; Aqlan, Faisal ; Apoorva, Anugu ; Olagunju, Ayodele. / Big data etl implementation approaches : A systematic literature review. Proceedings - SEKE 2018: 30th International Conference on Software Engineering and Knowledge Engineering. Knowledge Systems Institute Graduate School, 2018. pp. 714-715 (Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE).
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Nwokeji, JC, Aqlan, F, Apoorva, A & Olagunju, A 2018, Big data etl implementation approaches: A systematic literature review. in Proceedings - SEKE 2018: 30th International Conference on Software Engineering and Knowledge Engineering. Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE, vol. 2018-July, Knowledge Systems Institute Graduate School, pp. 714-715, 30th International Conference on Software Engineering and Knowledge Engineering, SEKE 2018, Redwood City, United States, 7/1/18. https://doi.org/10.18293/SEKE2018-152

Big data etl implementation approaches : A systematic literature review. / Nwokeji, Joshua C.; Aqlan, Faisal; Apoorva, Anugu; Olagunju, Ayodele.

Proceedings - SEKE 2018: 30th International Conference on Software Engineering and Knowledge Engineering. Knowledge Systems Institute Graduate School, 2018. p. 714-715 (Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE; Vol. 2018-July).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Nwokeji JC, Aqlan F, Apoorva A, Olagunju A. Big data etl implementation approaches: A systematic literature review. In Proceedings - SEKE 2018: 30th International Conference on Software Engineering and Knowledge Engineering. Knowledge Systems Institute Graduate School. 2018. p. 714-715. (Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE). https://doi.org/10.18293/SEKE2018-152