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A Survey of Extract–Transform–Load Technology

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  • Panos Vassiliadis

    (University of Ioannina, Greece)

Abstract

The software processes that facilitate the original loading and the periodic refreshment of the data warehouse contents are commonly known as Extraction-Transformation-Loading (ETL) processes. The intention of this survey is to present the research work in the field of ETL technology in a structured way. To this end, we organize the coverage of the field as follows: (a) first, we cover the conceptual and logical modeling of ETL processes, along with some design methods, (b) we visit each stage of the E-T-L triplet, and examine problems that fall within each of these stages, (c) we discuss problems that pertain to the entirety of an ETL process, and, (d) we review some research prototypes of academic origin.

Suggested Citation

  • Panos Vassiliadis, 2009. "A Survey of Extract–Transform–Load Technology," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 5(3), pages 1-27, July.
    • Handle: RePEc:igg:jdwm00:v:5:y:2009:i:3:p:1-27
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    Cited by:

    1. Zhan, Yuanzhu & Tan, Kim Hua, 2020. "An analytic infrastructure for harvesting big data to enhance supply chain performance," European Journal of Operational Research, Elsevier, vol. 281(3), pages 559-574.
    2. Lawson, James G. & Street, Daniel A., 2021. "Detecting dirty data using SQL: Rigorous house insurance case," Journal of Accounting Education, Elsevier, vol. 55(C).
    3. Benedict Bender & Clementine Bertheau & Tim Körppen & Hannah Lauppe & Norbert Gronau, 2022. "A proposal for future data organization in enterprise systems—an analysis of established database approaches," Information Systems and e-Business Management, Springer, vol. 20(3), pages 441-494, September.
    4. Johannes Schneider & Stefan Seidel & Marcus Basalla & Jan Brocke, 2023. "Reuse, Reduce, Support: Design Principles for Green Data Mining," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 65(1), pages 65-83, February.
    5. David Gil & Magnus Johnsson & Higinio Mora & Julian Szymanski, 2019. "Advances in Architectures, Big Data, and Machine Learning Techniques for Complex Internet of Things Systems," Complexity, Hindawi, vol. 2019, pages 1-3, March.
    6. Henrik tom Wörden & Florian Spreckelsen & Stefan Luther & Ulrich Parlitz & Alexander Schlemmer, 2024. "Mapping Hierarchical File Structures to Semantic Data Models for Efficient Data Integration into Research Data Management Systems," Data, MDPI, vol. 9(2), pages 1-15, January.

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