The evolution of data storage architectures: examining the secure value of the Data Lakehouse

Tharaka Ilayperuma · Jeewanie Jayasinghe Arachchige · Faiza Allah Bukhsh · Maya Daneva

17 citations0/0 files storedJournal of Data Information and Management

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Big Data and Business Intelligence Cloud Computing and Resource Management Advanced Database Systems and Queries

Abstract

Abstract The digital shift in society is making continuous growth of data. However, choosing a suitable storage architecture to efficiently store, process, and manage data from numerous sources remains a challenge. Currently, there are three storage architecture generations in practice, and the most recent one is Data Lakehouse. Given its novelty, limited research has been done into the rationale behind its introduction, strengths, and weaknesses. In order to fill this gap, this study aims to investigate the secure value (comparative strengths) of the data lakehouse architecture compared to data warehouse and data lake architectures. After conducting a comprehensive systematic literature review, we propose a data storage evolution model showing the comparative strengths and weaknesses of data warehouse, lake, and lakehouse architectures. With the use of the proposed model and expert interviews, this study demonstrates the secure value of the data lakehouse compared to the preceding architectures. In addition, the study presents a high-level view of the overlapping strengths of data Lakehouse with both data warehouse and data lake. In essence, the artifact produced by this study can be used to explain the rationale behind the evolution of data storage architectures. Further, the proposed model will help the practitioners in studying the trade-off between different architectures to offer recommendations. Finally, authors acknowledge that this study has several limitations, such as the limited sample size for the interviews and the bias due to the use of qualitative research approach. However, all the available measures were taken to minimize the effects of these limitations.

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2007A Design Science Research Methodology for Information Systems Research 2014Data-intensive applications, challenges, techniques and technologies: A survey on Big Data 2009Sample Size and Saturation in PhD Studies Using Qualitative Interviews 2012Sample Size Policy for Qualitative Studies Using In-Depth Interviews 2014Design Science Methodology for Information Systems and Software Engineering 2013Learning to use new technologies by older adults: Perceived difficulties, experimentation behaviour and usability 2003Fundamentals of Data Warehouses 2019Data lake management

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