Abstract
The increasing complexity and volume of organizational data have led to the emergence of the Data Mesh paradigm, a data architecture with a federated governance aimed at addressing the limitations of traditional monolithic data systems that has overlapping principles with the microservices architectural style. Although related work exists, the majority of architectural approaches regarding Data Mesh are conceptual, technology-centric or vendor specific. This paper introduces a Data Mesh Reference Architecture (RA) using the ArchiMate enterprise architecture modeling language, designed to assist organizations in implementing (or migrating towards) data mesh solutions. The RA comprises three main components: domain architecture, self-serve data platform architecture, and federated governance, which reflect the main Data Mesh principles. Through a systematic literature review, four data mesh archetypes (Pure, Semi-Pure, Hybrid, and Distributed) were identified, along with challenges, limitations, and motivational factors for adoption. A questionnaire-based validation among experts confirmed the RA’s utility, quality, and variability. However, practical validation was not conducted within this study. The study contributes to both literature and practice by offering a structured approach and a set of reference models for designing data mesh architectures. Future research can contribute to practical validation, assessment of RA-driven design efficiency, and extending the RA with domain-driven solution architectures.
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References
Goedegebuure, A., et al.: Data mesh: a systematic gray literature review. ACM Comput. Surv. (2024)
Podlesny, N.J., Kayem, A.V.D.M., Meinel, C.: CoK: a survey of privacy challenges in relation to data meshes. In: Strauss, C., Cuzzocrea, A., Kotsis, G., Tjoa, A.M., Khalil, I. (eds.) DEXA 2022. LNCS, vol. 13426, pp. 85–102. Springer, Cham (2022)
Falconi, M., Plebani, P.: Adopting data mesh principles to boost data sharing for clinical trials. In: 2023 IEEE International Conference on Digital Health (ICDH), pp. 298–306 (2023)
Pakrashi, A., Wallace, D., Namee, B.M., Greene, D., Guéret, C.: Cowmesh: a data-mesh architecture to unify dairy industry data for prediction and monitoring. Front. Artif. Intell. 6 (2023)
Dehghani, Z., Fowler, M.: Data Mesh: Delivering Data-driven Value at Scale. O’Reilly Media (2022)
Driessen, S., van den Heuvel, W.-J., Monsieur, G.: ProMoTe: a data product model template for data meshes. In: Almeida, J.P.A., Borbinha, J., Guizzardi, G., Link, S., Zdravkovic, J. (eds.) ER 2023. LNCS, vol. 14320, pp. 125–142. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-47262-6_7
Machado, I.A., Costa, C., Santos, M.Y.: Data mesh: concepts and principles of a paradigm shift in data architectures. Procedia Comput. Sci. 196, 263–271 (2022). International Conference on ENTERprise Information Systems / ProjMAN - International Conference on Project MANagement / HCist - International Conference on Health and Social Care Information Systems and Technologies 2021
Pongpech, W.A.: A distributed data mesh paradigm for an event-based smart communities monitoring product. Procedia Comput. Sci. 220, 584–591 (2023). The 14th International Conference on Ambient Systems, Networks and Technologies Networks (ANT) and The 6th International Conference on Emerging Data and Industry 4.0 (EDI40)
Ashraf, A., Hassan, A., Mahdi, H.: Key lessons from microservices for data mesh adoption. In: 2023 International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), pp. 1–8 (2023)
Jonkman, C.: Organisational maturity assessment during the paradigm shift from monoliths to data mesh - design science research in developing a data mesh maturity assessment model. Master’s thesis, TU Delft (2023). https://repository.tudelft.nl/record/uuid:294d7df5-511c-4149-9507-21be6379375d
Vestues, K., Hanssen, G.K., Mikalsen, M., Buan, T.A., Conboy, K.: Agile data management in NAV: a case study. In: Stray, V., Stol, K.-J., Paasivaara, M., Kruchten, P. (eds.) XP 2022, pp. 220–235. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-08169-9_14
Hooshmand, Y., Resch, J., Wischnewski, P., Patil, P.: From a monolithic PLM landscape to a federated domain and data mesh. Proc. Des. Soc. 2, 713–722 (2022)
Hendriks, K.W.: Data governance structures in data mesh architectures (2023)
Bode, J., Kühl, N., Kreuzberger, D., Holtmann, C.: Toward avoiding the data mess: industry insights from data mesh implementations. IEEE Access 12, 95402–95416 (2024)
Sedlak, B., et al.: Towards serverless data exchange within federations. In: Aiello, M., Barzen, J., Dustdar, S., Leymann, F. (eds.) SummerSOC 2023. CCIS, vol. 1847, pp. 144–153. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-45728-9_9
Vestues, K., Hanssen, G.K., Mikalsen, M., Buan, T.A., Conboy, K.: Agile data management in NAV: a case study (2022)
Hermawan, R.A., Sumitra, I.D.: Designing enterprise architecture using togaf architecture development method. In: IOP Conference Series: Materials Science and Engineering, vol. 662, no. 4, p. 042021 (2019)
dela Cruz, N., Tobin, M., Schenz, G., Barden, D.: Enterprise data architecture: development scenarios using ORM. In: Meersman, R., Dillon, T., Herrero, P. (eds.) OTM 2011. LNCS, vol. 7046, pp. 278–287. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25126-9_39
Sanyoto, A.E.A., Saputra, M.C.: Archimate’s strengths and weaknesses as EA modeling language: a systematic mapping study. In: 2023 Eighth International Conference on Informatics and Computing (ICIC), pp. 1–6 (2023)
Sang, G.M., Xu, L., de Vrieze, P.: Simplifying big data analytics systems with a reference architecture. In: Camarinha-Matos, L.M., Afsarmanesh, H., Fornasiero, R. (eds.) PRO-VE 2017. IAICT, vol. 506, pp. 242–249. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-65151-4_23
Carrera-Rivera, A., Ochoa, W., Larrinaga, F., Lasa, G.: How-to conduct a systematic literature review: a quick guide for computer science research. Methods X 9 (2022)
van der Werf, D.: Towards a data mesh: reference architecture. Master’s thesis, University of Twente (2024)
Strengholt, P.: Data Management at Scale: Modern Data Architecture with Data Mesh and Data Fabric. O’Reilly Media (2023)
Dibouliya, A., Jotwani, D.V.: Review on data mesh architecture and its impact. J. Harbin Eng. Univ. (2023)
Butte, V.K., Butte, S.: Enterprise data strategy: a decentralized data mesh approach. In: 2022 International Conference on Data Analytics for Business and Industry (ICDABI), pp. 62–66 (2022)
Kancharla, J.R., Kumar, S.M.: Breaking down data silos: data mesh to achieve effective aggregation in data localization. In: 2023 International Conference on Computer, Electronics & Electrical Engineering & their Applications (IC2E3), pp. 1–5 (2023)
Dončević, J., Fertalj, K., Brcic, M., Kovač, M.: Mask-mediator-wrapper architecture as a data mesh driver. IEEE Trans. Softw. Eng. 50(4), 900–910 (2024)
Dahdal, S., Poltronieri, F., Tortonesi, M., Stefanelli, C., Suri, N.: A data mesh approach for enabling data-centric applications at the tactical edge. In: 2023 International Conference on Military Communications and Information Systems (ICMCIS), pp. 1–9 (2023)
McEachen, N., Lewis, J.: Enabling knowledge sharing by managing dependencies and interoperability between interlinked spatial knowledge graphs. Int. Arch. Photogrammetry Remote Sens. Spatial Inf. Sci. XLVIII-4/W7-2023, 117–124 (2023)
Krystek, M., Morzy, M., Mazurek, C., Pukacki, J.: Introducing data mesh paradigm for smart city platforms design. In: Proceedings of the Annual Hawaii International Conference on System Sciences, vol. 2023-January, pp. 6885–6892 (2023)
Kraska, T., et al.: Check out the big brain on brad: simplifying cloud data processing with learned automated data meshes. Proc. VLDB Endow. 16(11), 3293–3301 (2023)
Angelov, S., Grefen, P., Greefhorst, D.: A framework for analysis and design of software reference architectures. Inf. Softw. Technol. 54(4), 417–431 (2012)
Galster, M., Avgeriou, P.: Empirically-grounded reference architectures: a proposal. In: Proceedings of the Joint ACM SIGSOFT Conference – QoSA and ACM SIGSOFT Symposium – ISARCS on Quality of Software Architectures – QoSA and Architecting Critical Systems – ISARCS, QoSA-ISARCS 2011, pp. 153–158. Association for Computing Machinery, New York (2011)
Wieringa, R.J.: What is Design Science?, pp. 3–11. Springer, Heidelberg (2014)
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van der Werf, D., Moreira, J., Piest, J.P.S. (2025). Towards a Data Mesh Reference Architecture. In: Kaczmarek-Heß, M., Rosenthal, K., Suchánek, M., Da Silva, M.M., Proper, H.A., Schnellmann, M. (eds) Enterprise Design, Operations, and Computing. EDOC 2024 Workshops . EDOC 2024. Lecture Notes in Business Information Processing, vol 537. Springer, Cham. https://doi.org/10.1007/978-3-031-79059-1_21
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