Scientific Literature

We want this collection of publications to be a community effort. Please share your observations and learnings with us so we can put them up here for you, or add them yourself via a pull request yourself.

This overview is curated by Jan Bode.

We provide an overview of peer-reviewed and unpublished contributions and their major takeaways.

Peer-reviewed Literature

A novel design for Data Processing Framework of Park-level Power System with Data Mesh concept
Li, J., Cai, S., Wang, L., Li, M., Li, J., & Tu, H. (2022). A novel design for Data Processing Framework of Park-level Power System with Data Mesh concept. 2022 IEEE International Conference on Energy Internet (ICEI), 153–158.
Source: doi.org/10.1109/ICEI57064.2022.00032
Takeaways
  • Design scheme for data processing using data mesh concept
  • Context: Park-level power system in China
  • Design components: Metadata center, Data service, Decoupling, Federated computing, Registry, Data infrastructure on the cloud, and Scheduling components
Enterprise Data Strategy: A Decentralized Data Mesh Approach
Butte, V. K., & Butte, S. (2022). Enterprise Data Strategy: A Decentralized Data Mesh Approach. 2022 International Conference on Data Analytics for Business and Industry, ICDABI 2022, 62–66.
Source: doi.org/10.1109/ICDABI56818.2022.10041672
Takeaways
  • Overview: Concepts, reference architectures
  • Generic architectures at domain and mesh level
  • Data mesh cloud architecture
From a Monolithic PLM Landscape to a Federated Domain and Data Mesh
Hooshmand, Y., Resch, J., Wischnewski, P., & Patil, P. (2022). From a Monolithic PLM Landscape to a Federated Domain and Data Mesh. Proceedings of the Design Society, 2, 713–722.
Source: doi.org/10.1017/PDS.2022.73
Takeaways
  • Transforming monolithic PLM landscape into a Data Mesh
  • Tools to overcome challenges in PLM landscape: Semantic web technology, Domain-driven design, Data mesh
  • Elements of proposed domain-driven landscape: Domain's context map, Domain structure and setup, Model-driven software components and services, Cross-layer semantic layer
  • Infrastructure for domain-driven PLM landscape: Domain agnostic, federated governance
CoK: A Survey of Privacy Challenges in Relation to Data Meshes
Podlesny, N. J., Kayem, A. V. D. M., & Meinel, C. (2022). CoK: A Survey of Privacy Challenges in Relation to Data Meshes. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13426 LNCS, 85–102.
Source: doi.org/10.1007/978-3-031-12423-57
Takeaways
  • Privacy challenges in data mesh due to distributed nature
  • Survey of privacy-related issues
  • Limitations of existing solutions
  • Need for performant algorithms to determine linkages across data repositories on-the-fly
Agile Data Management in NAV: A Case Study
Vestues, K., Hanssen, G. K., Mikalsen, M., Buan, T. A., & Conboy, K. (2022). Agile Data Management in NAV: A Case Study. Lecture Notes in Business Information Processing, 445 LNBIP, 220–235.
Source: doi.org/10.1007/978-3-031-08169-9_14
Takeaways
  • Case study: Norwegian Labor and Welfare Administration
  • 18 semi-structured expert interviews
  • Challenges when transitioning from centralized towards agile data management using data mesh
  • Challenges: Change of control, Access management, Create data products, Establish ecosystem
Advancing Data Architectures with Data Mesh Implementations
Araújo Machado, I., Costa, C., & Santos, M. Y. (2022). Advancing Data Architectures with Data Mesh Implementations. Lecture Notes in Business Information Processing, 452, 10–18.
Source: doi.org/10.1007/978-3-031-07481-3_2
Takeaways
  • Support for future data mesh implementations
  • Proposal for technological data mesh architecture
  • Evaluation and proof-of concept use case: Chocolate online seller
Finding Your Way Through the Jungle of Big Data Architectures
Priebe, T., Neumaier, S., & Markus, S. (2021). Finding Your Way Through the Jungle of Big Data Architectures. Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021, 5994–5996.
Source: doi.org/10.1109/BIGDATA52589.2021.9671862
Takeaways
  • Overview and comparison of current big data architectures: Logical data warehouse, Data fabric, Data mesh
  • Goal: Provide guidance in choosing the right architecture
  • Proposed architectural framework based on DAMA-DMBOK and ArchiMate
A Semantic Approach to Identifier Management in Engineering Systems
Mehmandarov, R., Waaler, A., Cameron, D., Fjellheim, R., & Pettersen, T. B. (2021). A Semantic Approach to Identifier Management in Engineering Systems. Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021, 4613–4616.
Source: doi.org/10.1109/BIGDATA52589.2021.9671515
Takeaways
  • Approach for identifier management to improve system integration and system-to-system communication
  • Main challenge: Identifier mapping in distributed architectures
  • Further challenges: Minimize domain expert - IT system knowledge gap, Data contextualization
  • Applicable for oil and gas industry, as well as similar sectors of engineering
Data governance in data mesh infrastructures: The Saxo bank case study
Joshi, D., Pratik, S., & Rao, M. P. (2021). Data governance in data mesh infrastructures: The Saxo bank case study. Proceedings of the International Conference on Electronic Business (ICEB), 21, 599–604.
Source: https://aisel.aisnet.org/iceb2021/52/
Takeaways
  • Proposal: Federated data governance in data mesh infrastructure
  • Key elements: Data catalogue, Data quality, Data ownership
  • Selected impacts: Ecosystem expansion, Innovation, UX improvements
  • Selected challenges: Compliance, Align business and technology, Tool integration, Culture and change management
  • Example case study and implementation guideline: Saxo Bank
Data Mesh: Concepts and Principles of a Paradigm Shift in Data Architectures
Machado, I. A., Costa, C., & Santos, M. Y. (2021). Data Mesh: Concepts and Principles of a Paradigm Shift in Data Architectures. Procedia Computer Science, 196, 263–271.
Source: doi.org/10.1016/J.PROCS.2021.12.013
Takeaways
  • Motivation for the emerging paradigm shift
  • Comprehensive overview: Data mesh features and approaches
  • Comparison of different approaches
  • Description of recent implementations at Zalando and Netflix
Data-Driven Information Systems: The Data Mesh Paradigm Shift
Machado, I., Costa, C., & Santos, M. Y. (2021). Data-Driven Information Systems: The Data Mesh Paradigm Shift. International Conference on Information Systems Development (ISD).
Source: https://aisel.aisnet.org/isd2014/proceedings2021/currenttopics/9
Takeaways
  • Contribution: Data mesh domain model and conceptual architecture
  • Domain model construct based on DATSIS principles and their relationships
  • Conceptual architecture based on: Security machanism, Nodes and catalog, Self-serve data platform, and Infrastructure
Exploring the Benefits of Blockchain-Powered Metadata Catalogs in Data Mesh Architecture
Anton Dolhopolov, Arnaud Castelltort, and Anne Laurent. 2023. Exploring the Benefits of Blockchain-Powered Metadata Catalogs in Data Mesh Architecture. 6TH WORKSHOP ON BLOCKCHAINS FOR INTER-ORGANIZATIONAL COLLABORATION (BIOC’23).
Source: https://hal.umontpellier.fr/hal-04156089/document
Takeaways
  • Formalisation of 3 types of metadata catalog: centralized, federated (distributed), decentralized
  • Proposal of a federated metadata catalog model based on blockchain technologies
Implementing a Blockchain-Powered Metadata Catalog in Data Mesh Architecture
Anton Dolhopolov, Arnaud Castelltort, and Anne Laurent. Implementing a Blockchain-Powered Metadata Catalog in Data Mesh Architecture. 5th International Blockchain Congress (2023).
Source: https://hal.umontpellier.fr/hal-04156134/document
Takeaways
  • Metadata catalog prototype based on Hyperledger Fabric
  • Using metadata model asset & Smart Contracts as automatic metadata governance tools
  • Data Product consumption workflow based on metadata propagation in the ledger

Unpublished Literature

Data Mesh: Motivational Factors, Challenges, and Best Practices under review
Bode, J., Kühl, N., Kreuzberger, D., Hirschl, S., & Holtmann, C. (2023). Data Mesh: Motivational Factors, Challenges, and Best Practices. ArXiv [Cs.AI].
Source: arxiv.org/abs/2302.01713
Takeaways
  • Learnings from recent cross-industry data mesh implementations
  • 15 semi-structured interviews with industry experts
  • Contribution: Motivational factors, challenges, best practices, impacts, and archetypes
  • Selected best practices: (Temporary) Cross-domain unit, Empower and observe, Conscious adoption, Dedicated ownership

Other Overviews of Scientific Literature

We want this collection of publications to be a community effort. Please share your observations and learnings with us so we can put them up here for you, or add them yourself via a pull request yourself.