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Data mesh

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Distributed architecture framework for data management
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Data mesh is a sociotechnical approach to building a decentralized data architecture by leveraging a domain-oriented, self-serve design (in a software development perspective), and borrows Eric Evans’ theory of domain-driven design and Manuel Pais’ and Matthew Skelton’s theory of team topologies. Data mesh mainly concerns itself with the data itself, taking the data lake and the pipelines as a secondary concern. The main proposition is scaling analytical data by domain-oriented decentralization. With data mesh, the responsibility for analytical data is shifted from the central data team to the domain teams, supported by a data platform team that provides a domain-agnostic data platform. This enables a decrease in data disorder or the existence of isolated data silos, due to the presence of a centralized system that ensures the consistent sharing of fundamental principles across various nodes within the data mesh and allows for the sharing of data across different areas.

History

The term data mesh was first defined by Zhamak Dehghani in 2019 while she was working as a principal consultant at the technology company Thoughtworks. Dehghani introduced the term in 2019 and then provided greater detail on its principles and logical architecture throughout 2020. The process was predicted to be a “big contender” for companies in 2022. Data meshes have been implemented by companies such as Zalando, Netflix, Intuit, VistaPrint, PayPal and others.

In 2022, Dehghani left Thoughtworks to found Nextdata Technologies to focus on decentralized data.

Principles

Data mesh is based on four core principles:

In addition to these principles, Dehghani writes that the data products created by each domain team should be discoverable, addressable, trustworthy, possess self-describing semantics and syntax, be interoperable, secure, and governed by global standards and access controls. In other words, the data should be treated as a product that is ready to use and reliable.

In practice

After its introduction in 2019 multiple companies started to implement a data mesh and share their experiences. Challenges (C) and best practices (BP) for practitioners, include:

C1. Federated data governance
Companies report difficulties to adopt a federated governance structure for activities and processes that were previously centrally owned and enforced. This is especially true for security, privacy, and regulatory topics.
C2. Responsibility shift
In data mesh individuals within domains are end-to-end responsible for data products. This new responsibility can be challenging, because it is rarely compensated and usually benefits other domains.
C3. Comprehension
Research has shown a severe lack of comprehension for the data mesh paradigm among employees of companies implementing a data mesh.
BP1. Cross-domain unit
Addressing C1, organizations should introduce a cross-domain steering unit responsible for strategic planning, use case prioritization, and the enforcement of specific governance rules—especially concerning security, regulatory, and privacy-related topics. Nevertheless, a cross-domain steering unit can only complement and support the federated governance structure and may grow obsolete with the increasing maturity of the data mesh.
BP2. Track and observe
Addressing C2., organizations should observe and score data product quality as tracking and ranking key data products can encourage high-quality offerings, motivate domain owners, and support budget negotiations.
BP3. Conscious adoption
Organizations should thoroughly assess and evaluate their existing data systems, consider organizational factors, and weigh the potential benefits before implementing a data mesh. When introducing data mesh, it is advised to carefully and consciously introduce data mesh terminology to ensure a clear understanding of the concept (C3).

Community

Scott Hirleman has started a data mesh community that contains over 7,500 people in their Slack channel.

See also

References

  1. Evans, Eric (2004). Domain-driven design : tackling complexity in the heart of software. Boston: Addison-Wesley. ISBN 0-321-12521-5. OCLC 52134890.
  2. Skelton, Matthew (2019). Team topologies : organizing business and technology teams for fast flow. Manuel Pais. Portland, OR. ISBN 978-1-942788-84-3. OCLC 1108538721.{{cite book}}: CS1 maint: location missing publisher (link)
  3. Machado, Inês Araújo; Costa, Carlos; Santos, Maribel Yasmina (2022-01-01). "Data Mesh: Concepts and Principles of a Paradigm Shift in Data Architectures". Procedia Computer Science. 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. 196: 263–271. doi:10.1016/j.procs.2021.12.013. hdl:1822/78127. ISSN 1877-0509. S2CID 245864612.
  4. "Data Mesh Architecture". datamesh-architecture.com. Retrieved 2022-06-13.
  5. Dehghani, Zhamak (2022). Data Mesh. Sebastopol, CA. ISBN 978-1-4920-9236-0. OCLC 1260236796.{{cite book}}: CS1 maint: location missing publisher (link)
  6. Machado, Inês Araújo; Costa, Carlos; Santos, Maribel Yasmina (2022-01-01). "Data Mesh: Concepts and Principles of a Paradigm Shift in Data Architectures". Procedia Computer Science. 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. 196: 263–271. doi:10.1016/j.procs.2021.12.013. hdl:1822/78127. ISSN 1877-0509.
  7. ^ "How to Move Beyond a Monolithic Data Lake to a Distributed Data Mesh". martinfowler.com. Retrieved 28 January 2022.
  8. Baer (dbInsight), Tony. "Data Mesh: Should you try this at home?". ZDNet. Retrieved 2022-02-10.
  9. Andy Mott (2022-01-12). "Driving Faster Insights with a Data Mesh". RTInsights. Retrieved 2022-03-01.
  10. "Developments that will define data governance and operational security in 2022". Help Net Security. 2021-12-28. Retrieved 2022-03-01.
  11. Bane, Andy. "Council Post: Where Is Industrial Transformation Headed In 2022?". Forbes. Retrieved 2022-03-01.
  12. ^ Schultze, Max; Wider, Arif (2021). Data Mesh in Practice. ISBN 978-1-09-810849-6.
  13. Netflix Data Mesh: Composable Data Processing - Justin Cunningham, retrieved 2022-04-29
  14. ^ Baker, Tristan (2021-02-22). "Intuit's Data Mesh Strategy". Intuit Engineering. Retrieved 2022-04-29.
  15. ^ "The next generation of Data Platforms is the Data Mesh". 2022-08-03. Retrieved 2023-02-08.
  16. "Why We Started Nextdata". 2022-01-16. Retrieved 2023-02-08.
  17. Dehghani, Zhamak (2022). Data Mesh. Sebastopol, CA. ISBN 978-1-4920-9236-0. OCLC 1260236796.{{cite book}}: CS1 maint: location missing publisher (link)
  18. "Data Mesh defined | James Serra's Blog". 16 February 2021. Retrieved 28 January 2022.
  19. "Analytics in 2022 Means Mastery of Distributed Data Politics". The New Stack. 2021-12-29. Retrieved 2022-03-03.
  20. "Developments that will define data governance and operational security in 2022". Help Net Security. 2021-12-28. Retrieved 2022-03-01.
  21. "Looking Glass | 2024". Thoughtworks. Retrieved 2024-10-10.
  22. ^ Bode, Jan; Kühl, Niklas; Kreuzberger, Dominik; Hirschl, Sebastian; Holtmann, Carsten (2023-05-04). "Data Mesh: Motivational Factors, Challenges, and Best Practices". arXiv:2302.01713v2 .
  23. ^ Vestues, Kathrine; Hanssen, Geir Kjetil; Mikalsen, Marius; Buan, Thor Aleksander; Conboy, Kieran (2022). "Agile Data Management in NAV: A Case Study". Agile Processes in Software Engineering and Extreme Programming. Lecture Notes in Business Information Processing 445 LNBIP. Vol. 445. Springer. pp. 220–235. doi:10.1007/978-3-031-08169-9_14. ISBN 978-3-031-08168-2.
  24. Joshi, Divya; Pratik, Sheetal; Rao, Madhu Podila (2021). "Data Governance in Data Mesh Infrastructures: The Saxo Bank Case Study". Proceedings of the International Conference on Electronic Business (ICEB). Vol. 21. pp. 599–604.
  25. Whyte, Martin; Odenkirchen, Andreas; Bautz, Stephan; Heringer, Agnes; Krukow, Oliver (2022). "Data Mesh - Just another buzzword or the next generation data platform?". PwC study 2022: Changing data platforms.
  26. "The Global Home for Data Mesh". The Global Home for Data Mesh. Retrieved 2022-04-24.
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