Modern Data Architectures: Evaluation Framework for Selecting Suitable Data Platforms
Abstract
Purpose: This paper addresses the challenge of selecting a suitable modern data architecture in the context of growing data complexity, increased demand for real-time analytics, and evolving business needs.
Methodology/Approach: The study follows the DSR process. The paper presents a structured evaluation framework based on clearly defined criteria across technical, organisational, and economic dimensions. The framework supports decision-makers in comparing data architectures, including Data Warehouse, Data Lake, and Data Lakehouse, through a weighted scoring system.
Findings: The outcome highlights the advantages of the Data Lakehouse paradigm for the evaluating organisation, which sought to combine flexibility, scalability, and advanced analytics capabilities. This paper contributes a practical and adaptable methodology that aligns enterprise and data architecture decisions.
Research Limitation/Implications: Since each question may hold varying importance for the evaluator, it is recommended that each individual question be weighted. The evaluator must possess the necessary knowledge to assign weights.
Originality/Value of paper: The methodology provides a foundation for further research on data architectures and their evaluation. It can serve as a starting point for the development of analytical tools and the implementation of case studies.
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Authors
Copyright (c) 2025 Felix Espinoza, Milos Maryska, Petr Doucek
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