A Narrative Review of Data Mesh Architecture Principles and Implementation Outcomes
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Abstract
Centralised data architectures often create operational bottlenecks that limit organisational agility. Data Mesh offers a distributed alternative through domain ownership and federated governance. This narrative review synthesises 52 sources published between 2001 and 2024, examining the evolution from traditional data architectures to Data Mesh implementations across financial services, healthcare, e-commerce, and technology sectors. The review traces the progression from centralised data warehouses through distributed computing frameworks to Data Mesh's emergence, identifying four foundational principles domain-oriented decentralisation, data as a product, self-serve infrastructure, and federated governance. Analysis of recent implementation studies reveals mixed outcomes. Successful adoptions demonstrate improved domain autonomy and reduced central bottlenecks. However, multiple case reports significant coordination complexity and extended implementation timelines, with transformations requiring substantial investments in platform engineering. Consistent challenges emerge, including skill gaps in domain teams transitioning to data ownership, policy conflicts in federated governance structures, infrastructure investments that exceed traditional architectures, and cultural resistance to distributed accountability. Implementation success correlates with existing DevOps maturity, sustained executive sponsorship, phased adoption approaches, and robust metadata management capabilities. The review identifies critical research gaps in standardised success metrics, quantitative failure analysis, privacy-preserving techniques for federated environments, and long-term sustainability assessment. Based on the analysed cases, Data Mesh appears most suitable for large enterprises with diverse data domains and established platform engineering capabilities. Smaller organisations may find centralised approaches more appropriate given the complexity and resource requirements of distributed architectures. This synthesis provides practitioners with evidence-based insights while highlighting priorities for future research.
Manuscript received: 9 Jun 2025 | Revised: 24 Jul 2025 | Accepted: 30 Jul 2025 | Published: 30 Nov 2025
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References
N.E. Moukhi, I.E. Azami and A. Mouloudi, "Data warehouse state of the art and future challenges," 2015 International Conference on Cloud Technologies and Applications (CloudTech), pp. 1–6, 2015.
DOI: https://doi.org/10.1109/CloudTech.2015.7337004
M. Rifaie, K. Kianmehr, R. Alhajj and M. J. Ridley, "Data warehouse architecture and design," 2008 IEEE International Conference on Information Reuse and Integration, pp. 58–63, 2008.
DOI: https://doi.org/10.1109/IRI.2008.4583005
H.-F. Qin, Z.-M. Qian and Y.-C. Zhao, "On the Research of Data Warehouse in Big Data," 2015 International Conference on Network and Information Systems for Computers, pp. 354–357, 2015.
DOI: https://doi.org/10.1109/ICNISC.2015.126
G. Furlow, "The case for building a data warehouse," IT Professional, vol. 3, no. 4, pp. 31–34, 2001.
DOI: https://doi.org/10.1109/6294.946616
M.E. Conway, "How Do Committees Invent?," Datamation, vol. 14, no. 4, pp. 28–31, 1968.
URL: https://www.melconway.com/Home/pdf/committees.pdf
B.K. Seah and N.E. Selan, "Design and implementation of data warehouse with data model using survey-based services data," Fourth edition of the International Conference on the Innovative Computing Technology (INTECH 2014), pp. 58–64, 2014.
DOI: https://doi.org/10.1109/INTECH.2014.6927748
R.J. Santos, J. Bernardino and M. Vieira, "A survey on data security in data warehousing: Issues, challenges and opportunities," 2011 IEEE EUROCON - International Conference on Computer as a Tool, pp. 1–4, 2011.
DOI: https://doi.org/10.1109/EUROCON.2011.5929314
K. Liu, M. Yang, X. Li, K. Zhang, X. Xia and H. Yan, "M-Data-Fabric: A Data Fabric System Based on Metadata," 2022 IEEE 5th International Conference on Big Data and Artificial Intelligence (BDAI), pp. 57–62, 2022.
DOI: https://doi.org/10.1109/BDAI56143.2022.9862807
X. Li, M. Yang, X. Xia, K. Zhang and K. Liu, "A Distributed Data Fabric Architecture based on Metadate Knowledge Graph," 2022 5th International Conference on Data Science and Information Technology (DSIT), pp. 1–7, 2022.
DOI: https://doi.org/10.1109/DSIT55514.2022.9943831
T. Priebe, S. Neumaier and S. Markus, "Finding Your Way Through the Jungle of Big Data Architectures," 2021 IEEE International Conference on Big Data (Big Data), pp. 5994–5996, 2021.
DOI: https://doi.org/10.1109/BigData52589.2021.9671862
A. Macías, D. Muñoz, E. Navarro and P. González, "Data fabric and digital twins: An integrated approach for data fusion design and evaluation of pervasive systems," Information Fusion, vol. 103, p. 102139, 2024.
DOI: https://doi.org/10.1016/j.inffus.2023.102139
N. Dragoni, M. Caramia, A. Meluzzi, F. Mosca, R. Rapini, and A. Spalazzi, "Microservices: yesterday, today, and tomorrow," Present and Ulterior Software Engineering, 2017.
DOI: https://doi.org/10.1007/978-3-319-67425-4_12
K. Bakshi, "Microservices-based software architecture and approaches," 2017 IEEE Aerospace Conference, pp. 1–8, 2017.
DOI: https://doi.org/10.1109/AERO.2017.7943959
G. Liu, B. Huang, Z. Liang, M. Qin, H. Zhou and Z. Li, "Microservices: architecture, container, and challenges," 2020 IEEE 20th International Conference on Software Quality, Reliability and Security Companion (QRS-C), pp. 629–635, 2020.
DOI: https://doi.org/10.1109/QRS-C51114.2020.00107
P.D. Francesco, "Architecting Microservices," 2017 IEEE International Conference on Software Architecture Workshops (ICSAW), pp. 224–229, 2017.
DOI: https://doi.org/10.1109/ICSAW.2017.65
R.M. Munaf, J. Ahmed, F. Khakwani and T. Rana, "Microservices Architecture: Challenges and Proposed Conceptual Design," 2019 International Conference on Communication Technologies (ComTech), pp. 82–87, 2019.
DOI: https://doi.org/10.1109/COMTECH.2019.8737831
D. Talia, "A view of programming scalable data analysis: from clouds to exascale," Journal of Cloud Computing, vol. 8, no. 1, p. 4, 2019.
DOI: https://doi.org/10.1186/s13677-019-0127-x
C.S. Lee, Y. Kim, Y. Kim, J. Kim, S. Lee and B.R. Lee, "A Case Study of Data Management Challenges Presented in Large-Scale Machine Learning Workflows," 2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing (CCGrid), pp. 71–81, 2023.
DOI: https://doi.org/10.1109/CCGrid57682.2023.00017
D. Wang, Q. Li, C. Xu, P. Wang and Z. Wang, "Research of Data Warehouse for Science and Technology Management System," 2021 International Conference on Service Science (ICSS), pp. 65–69, 2021.
DOI: https://doi.org/10.1109/ICSS53362.2021.00018
G. Garani, A. Chernov, I. Savvas and M. Butakova, "A Data Warehouse Approach for Business Intelligence," 2019 IEEE 28th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE), pp. 70–75, 2019.
DOI: https://doi.org/10.1109/WETICE.2019.00022
I.A. Machado, C. Costa and M.Y. Santos, "Data Mesh: Concepts and Principles of a Paradigm Shift in Data Architectures," Procedia Computer Science, vol. 196, pp. 263–271, 2022.
DOI: https://doi.org/10.1016/j.procs.2021.12.013
S. Lungu and M. Nyirenda, "Current Trends in the Management of Distributed Transactions in Micro-Services Architectures: A Systematic Literature Review," Open Journal of Applied Sciences, vol. 14, no. 09, pp. 2519–2543, 2024.
DOI: https://doi.org/10.4236/ojapps.2024.149167
N. Polyzotis, S. Roy, S.E. Whang and M. Zinkevich, "Data Management Challenges in Production Machine Learning," Proceedings of the 2017 ACM International Conference on Management of Data, pp. 1723–1726, 2017.
DOI: https://doi.org/10.1145/3035918.3054782
R.Y. Wang and D.M. Strong, "Beyond Accuracy: What Data Quality Means to Data Consumers," Journal of Management Information Systems, vol. 12, no. 4, pp. 5–33, 1996.
DOI: https://doi.org/10.1080/07421222.1996.11518099
F. Li, B.C. Ooi, M.T. Özsu and S. Wu, "Distributed data management using MapReduce," ACM Computing Surveys, vol. 46, no. 3, 2014.
DOI: https://doi.org/10.1145/2503009
D. Taibi, V. Lenarduzzi and C. Pahl, "Processes, Motivations, and Issues for Migrating to Microservices Architectures: An Empirical Investigation," IEEE Cloud Computing, vol. 4, no. 5, pp. 22–32, 2017.
DOI: https://doi.org/10.1109/MCC.2017.4250931
L. Chen and M.A. Babar, "A systematic review of evaluation of variability management approaches in software product lines," Information and Software Technology, vol. 53, no. 4, pp. 344–362, 2011.
DOI: https://doi.org/10.1016/j.infsof.2010.12.006
M. Janssen and J.V.D. Hoven, "Big and Open Linked Data (BOLD) in government: A challenge to transparency and privacy?," Government Information Quarterly, vol. 32, no. 4, pp. 363–368, 2015.
DOI: https://doi.org/10.1016/j.giq.2015.11.007
J.M. Hellerstein, V. Sreekanti, J.E. Gonzalez, J. Dalton, A. Dey, S. Nag, K. Ramachandran, S. Arora, A. Bhattacharyya, S. Das, M. Donsky, G. Fierro, C. She, C. Steinbach, V. Subramanian and E. Sun, "Ground: A Data Context Service," 8th Biennial Conference on Innovative Data Systems Research, 2017.
URL:http://cidrdb.org/cidr2017/papers/p111-hellerstein-cidr17.pdf
D. Sculley, G. Holt, D. Golovin, E. Davydov, T. Phillips, D. Ebner, V. Chaudhary, and M. Young, "Hidden Technical Debt in Machine Learning Systems," Advances in Neural Information Processing Systems 28, pp. 2503–2511, 2015.
T. Dybå and T. Dingsøyr, "Empirical studies of agile software development: A systematic review," Information and Software Technology, vol. 50, no. 9–10, pp. 833–859, 2008.
DOI: https://doi.org/10.1016/j.infsof.2008.01.006
T. Kraska, A. Talwalkar, J. Duchi, R. Griffith, M.J. Franklin, and M. Jordan, "MLbase: A Distributed Machine-learning System," Proceedings of the 6th Biennial Conference on Innovative Data Systems Research, pp. 1–7, 2013.
URL:https://www.cidrdb.org/cidr2013/Papers/CIDR13_Paper118.pdf
A.H. Chillón, M. Klettke, D.S. Ruiz and J.G. Molina, "A Generic Schema Evolution Approach for NoSQL and Relational Databases," IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 7, pp. 2774–2789, 2024.
DOI: https://doi.org/10.1109/TKDE.2024.3362273
T. Hu, T. Wang and Q. Zhou, "Online schema evolution is (almost) free for snapshot databases," Proceedings of the VLDB Endowment, vol. 16, no. 2, pp. 140–153, 2022.
DOI: https://doi.org/10.14778/3565816.3565818
A. Wider, S. Verma and A. Akhtar, "Decentralised Data Governance as Part of a Data Mesh Platform: Concepts and Approaches," 2023 IEEE International Conference on Web Services (ICWS), pp. 746–754, 2023.
DOI: https://doi.org/10.1109/ICWS60048.2023.00101
A. Goedegebuure, J. Hillebrand, J. Garrevoet and M. Van Keulen, “Data Mesh: A Systematic Gray Literature Review,” ACM Computing Surveys, vol. 57, no. 1, 2024.
DOI: https://doi.org/10.1145/3687301
S.F. Lameh, W. Noble, Y. Amannejad and A. Afshar, "Analysis of Federated Learning as a Distributed Solution for Learning on Edge Devices," 2020 International Conference on Intelligent Data Science Technologies and Applications (IDSTA), pp. 66–74, 2020.
DOI: https://doi.org/10.1109/IDSTA50958.2020.9264060
H. Jeung, A.K. Mokashi, H.V. Jagadish and J.M. Hellerstein, "Effective Metadata Management in Federated Sensor Networks," 2010 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing, pp. 107–114, 2010.
DOI: https://doi.org/10.1109/SUTC.2010.29
V. Yandrapalli, "AI-Powered Data Governance: A Cutting-Edge Method for Ensuring Data Quality for Machine Learning Applications," 2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE), pp. 1–6, 2024.
DOI: https://doi.org/10.1109/ic-ETITE58242.2024.10493601
Y.A. Bena, A. Benatia, Y. Ghoumari, S. Oughdir and M.E. Koutbi, "Big Data Governance Challenges Arising From Data Generated by Intelligent Systems Technologies: A Systematic Literature Review," IEEE Access, vol. 13, pp. 12859–12888, 2025.
DOI: https://doi.org/10.1109/ACCESS.2025.3528941
S. Shrivastava, R. Singh, S. Mittal, N. Agrawal and S. Chaudhuri, "DQA: Scalable, Automated and Interactive Data Quality Advisor," 2019 IEEE International Conference on Big Data (Big Data), pp. 2913–2922, 2019.
DOI: https://doi.org/10.1109/BigData47090.2019.9006187
H. Hong, S. Yoo, Y. Jin, and Y. Jang, "How Can We Improve Data Quality for Machine Learning? A Visual Analytics System using Data and Process-driven Strategies," 2023 IEEE 16th Pacific Visualization Symposium (PacificVis), pp. 112–121, 2023.
DOI: https://doi.org/10.1109/PacificVis56936.2023.00020
T.V. Eijk, I. Kumara, D.D. Nucci, D.A. Tamburri and W.-J.V.D. Heuvel, "Architectural Design Decisions for Self-Serve Data Platforms in Data Meshes," 2024 IEEE 21st International Conference on Software Architecture (ICSA), pp. 135-145, 2024.
DOI:https://doi.org/10.1109/ICSA59870.2024.00021
A. Wider, K. Jarmul and A. Akhtar, "Towards Automating Federated Data Governance," 2024 IEEE International Conference on Web Services (ICWS), pp. 10–19, 2024.
DOI: https://doi.org/10.1109/ICWS62655.2024.00019
I. Kumara, S. Driessen, T.V. Eijk, D.D. Nucci, D.A. Tamburri and W.-J.V.D. Heuvel, "Data Mesh Architecture: From Theory to Practice," 2024 IEEE 21st International Conference on Software Architecture Companion (ICSA-C), pp. 375–376, 2024.
DOI: https://doi.org/10.1109/ICSA-C63560.2024.00068
J. Bode, N. Kühl, D. Kreuzberger, S. Hirschl and C. Holtmann, "Towards Avoiding the Data Mess: Industry Insights from Data Mesh Implementations," IEEE Access, vol. 12, pp. 95402-95416, 2024.
DOI: https://doi.org/10.1109/ACCESS.2024.3417291
M. Falconi and P. Plebani, "Adopting Data Mesh principles to Boost Data Sharing for Clinical Trials," 2023 IEEE International Conference on Digital Health (ICDH), pp. 298–306, 2023.
DOI: https://doi.org/10.1109/ICDH60066.2023.00051
S. Dahdal, F. Poltronieri, M. Tortonesi, C. Stefanelli and N. Suri, "A Data Mesh Approach for Enabling Data-Centric Applications at the Tactical Edge," 2023 International Conference on Military Communications and Information Systems (ICMCIS), pp. 1–9, 2023.
DOI: https://doi.org/10.1109/ICMCIS59922.2023.10253568
A. Ashraf, A. Hassan and H. Mahdi, "Key Lessons from Microservices for Data Mesh Adoption," 2023 International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), pp. 1–8, 2023.
DOI: https://doi.org/10.1109/MIUCC58832.2023.10278300
S.A. Khan, I.H. Syed and J.I. Jawaid Iqbal, "From Signatures to AI: A Comprehensive Review of DDoS Detection Strategies in IoT & SDN," International Journal on Robotics, Automation and Sciences, vol. 7, no. 1, pp. 19–26, 2025.
DOI: https://doi.org/10.33093/ijoras.2025.7.1.3
B.A. Rachman, A.L. Maukar and J.K. Runtuk, "Vendor Evaluation and Selection for Forwarding Activities Using Stepwise Weight Assessment Analysis-Combined Compromise Solution (SWARA-CoCoSo) Method," International Journal on Robotics, Automation and Sciences, vol. 7, no. 1, pp. 27–34, 2025.