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'''Data mesh''' is a [[Sociotechnical system|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]]<ref>{{Cite book |last=Evans |first=Eric |url=https://www.worldcat.org/oclc/52134890 |title=Domain-driven design : tackling complexity in the heart of software |date=2004 |publisher=Addison-Wesley |isbn=0-321-12521-5 |location=Boston |oclc=52134890}}</ref> and Manuel Pais’ and Matthew Skelton’s theory of team topologies.<ref>{{Cite book |last=Skelton |first=Matthew |url=https://www.worldcat.org/oclc/1108538721 |title=Team topologies : organizing business and technology teams for fast flow |date=2019 |others=Manuel Pais |isbn=978-1-942788-84-3 |location=Portland, OR |oclc=1108538721}}</ref> Data mesh mainly concerns itself with the data itself, taking the [[data lake]] and the pipelines as a secondary concern. <ref>{{Cite journal |last1=Machado |first1=Inês Araújo |last2=Costa |first2=Carlos |last3=Santos |first3=Maribel Yasmina |date=2022-01-01 |title=Data Mesh: Concepts and Principles of a Paradigm Shift in Data Architectures |journal=Procedia Computer Science |series=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 |language=en |volume=196 |pages=263–271 |doi=10.1016/j.procs.2021.12.013 |s2cid=245864612 |issn=1877-0509|doi-access=free }}</ref> The main proposition is scaling analytical data by domain-oriented decentralization.<ref>{{Cite web |title=Data Mesh Architecture |url=https://datamesh-architecture.com/ |access-date=2022-06-13 |website=datamesh-architecture.com |language=en}}</ref> With data mesh, the responsibility for analytical data is shifted from the central data team to the domain teams, supported by a [[Data management platform|data platform]] team that provides a domain-agnostic data platform.<ref>{{Cite book |last=Dehghani |first=Zhamak |url=https://www.worldcat.org/oclc/1260236796 |title=Data Mesh |date=2022 |isbn=978-1-4920-9236-0 |location=Sebastopol, CA |oclc=1260236796}}</ref>
'''Data mesh''' is a [[Sociotechnical system|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]]<ref>{{Cite book |last=Evans |first=Eric |url=https://www.worldcat.org/oclc/52134890 |title=Domain-driven design : tackling complexity in the heart of software |date=2004 |publisher=Addison-Wesley |isbn=0-321-12521-5 |location=Boston |oclc=52134890}}</ref> and Manuel Pais’ and Matthew Skelton’s theory of team topologies.<ref>{{Cite book |last=Skelton |first=Matthew |url=https://www.worldcat.org/oclc/1108538721 |title=Team topologies : organizing business and technology teams for fast flow |date=2019 |others=Manuel Pais |isbn=978-1-942788-84-3 |location=Portland, OR |oclc=1108538721}}</ref> Data mesh mainly concerns itself with the data itself, taking the [[data lake]] and the pipelines as a secondary concern. <ref>{{Cite journal |last1=Machado |first1=Inês Araújo |last2=Costa |first2=Carlos |last3=Santos |first3=Maribel Yasmina |date=2022-01-01 |title=Data Mesh: Concepts and Principles of a Paradigm Shift in Data Architectures |journal=Procedia Computer Science |series=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 |language=en |volume=196 |pages=263–271 |doi=10.1016/j.procs.2021.12.013 |s2cid=245864612 |issn=1877-0509|doi-access=free |hdl=1822/78127 |hdl-access=free }}</ref> The main proposition is scaling analytical data by domain-oriented decentralization.<ref>{{Cite web |title=Data Mesh Architecture |url=https://datamesh-architecture.com/ |access-date=2022-06-13 |website=datamesh-architecture.com |language=en}}</ref> With data mesh, the responsibility for analytical data is shifted from the central data team to the domain teams, supported by a [[Data management platform|data platform]] team that provides a domain-agnostic data platform.<ref>{{Cite book |last=Dehghani |first=Zhamak |url=https://www.worldcat.org/oclc/1260236796 |title=Data Mesh |date=2022 |isbn=978-1-4920-9236-0 |location=Sebastopol, CA |oclc=1260236796}}</ref> 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.<ref>{{Cite journal |last=Machado |first=Inês Araújo |last2=Costa |first2=Carlos |last3=Santos |first3=Maribel Yasmina |date=2022-01-01 |title=Data Mesh: Concepts and Principles of a Paradigm Shift in Data Architectures |url=https://www.sciencedirect.com/science/article/pii/S1877050921022365 |journal=Procedia Computer Science |series=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 |volume=196 |pages=263–271 |doi=10.1016/j.procs.2021.12.013 |issn=1877-0509|hdl=1822/78127 |hdl-access=free }}</ref>


== History ==
== History ==
The term ''data mesh'' was first defined by [[Zhamak Dehghani]] in 2019<ref name="Dehghani2019">{{cite web|url=https://martinfowler.com/articles/data-monolith-to-mesh.html|title=How to Move Beyond a Monolithic Data Lake to a Distributed Data Mesh|work=martinfowler.com|access-date=28 January 2022}}</ref> while she was working as a principal consultant at the technology company [[Thoughtworks]].<ref>{{Cite web |last=Baer (dbInsight) |first=Tony |title=Data Mesh: Should you try this at home? |url=https://www.zdnet.com/article/data-mesh-should-you-try-this-at-home/ |access-date=2022-02-10 |website=ZDNet |language=en}}</ref><ref>{{Cite web |last=Andy Mott |date=2022-01-12 |title=Driving Faster Insights with a Data Mesh |url=https://www.rtinsights.com/driving-faster-insights-with-a-data-mesh/ |access-date=2022-03-01 |website=RTInsights |language=en-US}}</ref> 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.<ref>{{Cite web |date=2021-12-28 |title=Developments that will define data governance and operational security in 2022 |url=https://www.helpnetsecurity.com/2021/12/28/data-governance-2022/ |access-date=2022-03-01 |website=Help Net Security |language=en-US}}</ref><ref>{{Cite web |last=Bane |first=Andy |title=Council Post: Where Is Industrial Transformation Headed In 2022? |url=https://www.forbes.com/sites/forbestechcouncil/2022/01/13/where-is-industrial-transformation-headed-in-2022/ |access-date=2022-03-01 |website=Forbes |language=en}}</ref> Data meshes have been implemented by companies such as [[Zalando]],<ref name="Schultze2021">{{Cite book |last1=Schultze |first1=Max |title=Data Mesh in Practice |last2=Wider |first2=Arif |year=2021 |isbn=978-1-09-810849-6}}</ref> [[Netflix]],<ref>{{Citation |title=Netflix Data Mesh: Composable Data Processing - Justin Cunningham |url=https://www.youtube.com/watch?v=TO_IiN06jJ4 |language=en |access-date=2022-04-29}}</ref> [[Intuit]],<ref name="Baker2021">{{Cite web |last=Baker |first=Tristan |date=2021-02-22 |title=Intuit's Data Mesh Strategy |url=https://medium.com/intuit-engineering/intuits-data-mesh-strategy-778e3edaa017 |access-date=2022-04-29 |website=Intuit Engineering |language=en}}</ref> [[VistaPrint]], [[PayPal]]<ref name="paypal2022">{{Cite web |date=2022-08-03 |title= The next generation of Data Platforms is the Data Mesh |url=https://medium.com/paypal-tech/the-next-generation-of-data-platforms-is-the-data-mesh-b7df4b825522/ |access-date=2023-02-08 |language=en-US}}</ref> and others.
The term ''data mesh'' was first defined by Zhamak Dehghani in 2019<ref name="Dehghani2019">{{cite web|url=https://martinfowler.com/articles/data-monolith-to-mesh.html|title=How to Move Beyond a Monolithic Data Lake to a Distributed Data Mesh|work=martinfowler.com|access-date=28 January 2022}}</ref> while she was working as a principal consultant at the technology company [[Thoughtworks]].<ref>{{Cite web |last=Baer (dbInsight) |first=Tony |title=Data Mesh: Should you try this at home? |url=https://www.zdnet.com/article/data-mesh-should-you-try-this-at-home/ |access-date=2022-02-10 |website=ZDNet |language=en}}</ref><ref>{{Cite web |last=Andy Mott |date=2022-01-12 |title=Driving Faster Insights with a Data Mesh |url=https://www.rtinsights.com/driving-faster-insights-with-a-data-mesh/ |access-date=2022-03-01 |website=RTInsights |language=en-US}}</ref> 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.<ref>{{Cite web |date=2021-12-28 |title=Developments that will define data governance and operational security in 2022 |url=https://www.helpnetsecurity.com/2021/12/28/data-governance-2022/ |access-date=2022-03-01 |website=Help Net Security |language=en-US}}</ref><ref>{{Cite web |last=Bane |first=Andy |title=Council Post: Where Is Industrial Transformation Headed In 2022? |url=https://www.forbes.com/sites/forbestechcouncil/2022/01/13/where-is-industrial-transformation-headed-in-2022/ |access-date=2022-03-01 |website=Forbes |language=en}}</ref> Data meshes have been implemented by companies such as [[Zalando]],<ref name="Schultze2021">{{Cite book |last1=Schultze |first1=Max |title=Data Mesh in Practice |last2=Wider |first2=Arif |year=2021 |isbn=978-1-09-810849-6}}</ref> [[Netflix]],<ref>{{Citation |title=Netflix Data Mesh: Composable Data Processing - Justin Cunningham |url=https://www.youtube.com/watch?v=TO_IiN06jJ4 |language=en |access-date=2022-04-29}}</ref> [[Intuit]],<ref name="Baker2021">{{Cite web |last=Baker |first=Tristan |date=2021-02-22 |title=Intuit's Data Mesh Strategy |url=https://medium.com/intuit-engineering/intuits-data-mesh-strategy-778e3edaa017 |access-date=2022-04-29 |website=Intuit Engineering |language=en}}</ref> [[VistaPrint]], [[PayPal]]<ref name="paypal2022">{{Cite web |date=2022-08-03 |title= The next generation of Data Platforms is the Data Mesh |url=https://medium.com/paypal-tech/the-next-generation-of-data-platforms-is-the-data-mesh-b7df4b825522/ |access-date=2023-02-08 |language=en-US}}</ref> and others.


In 2022, Dehghani left [[Thoughtworks]] to found [[Nextdata Technologies]] to focus on decentralized data.<ref>{{Cite web |date=2022-01-16 |title= Why We Started Nextdata |url=https://medium.com/@zhamakd/why-we-started-nextdata-dd30b8528fca/ |access-date=2023-02-08 |language=en-US}}</ref>
In 2022, Dehghani left [[Thoughtworks]] to found Nextdata Technologies to focus on decentralized data.<ref>{{Cite web |date=2022-01-16 |title= Why We Started Nextdata |url=https://medium.com/@zhamakd/why-we-started-nextdata-dd30b8528fca/ |access-date=2023-02-08 |language=en-US}}</ref>


== Principles ==
== Principles ==
Line 21: Line 21:
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.<ref>{{Cite web |date=2021-12-29 |title=Analytics in 2022 Means Mastery of Distributed Data Politics |url=https://thenewstack.io/analytics-in-2022-means-mastery-of-distributed-data-politics/ |access-date=2022-03-03 |website=The New Stack |language=en-US}}</ref> In other words, the data should be treated as a product that is ready to use and reliable.<ref>{{Cite web |date=2021-12-28 |title=Developments that will define data governance and operational security in 2022 |url=https://www.helpnetsecurity.com/2021/12/28/data-governance-2022/ |access-date=2022-03-01 |website=Help Net Security |language=en-US}}</ref>
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.<ref>{{Cite web |date=2021-12-29 |title=Analytics in 2022 Means Mastery of Distributed Data Politics |url=https://thenewstack.io/analytics-in-2022-means-mastery-of-distributed-data-politics/ |access-date=2022-03-03 |website=The New Stack |language=en-US}}</ref> In other words, the data should be treated as a product that is ready to use and reliable.<ref>{{Cite web |date=2021-12-28 |title=Developments that will define data governance and operational security in 2022 |url=https://www.helpnetsecurity.com/2021/12/28/data-governance-2022/ |access-date=2022-03-01 |website=Help Net Security |language=en-US}}</ref>


== Data mesh in practice ==
== In practice ==
After its introduction in 2017<ref name="Dehghani2019"/> multiple companies started to implement a data mesh<ref name="Schultze2021"/><ref name="Baker2021"/><ref name="paypal2022"/> and share their experiences. Challenges (C) and best practices (BP) for practitioners, include:
After its introduction in 2019<ref name="Dehghani2019"/> multiple companies started to implement a data mesh<ref name="Schultze2021"/><ref name="Baker2021"/><ref name="paypal2022"/> 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.<ref name="Bode2023">{{cite arXiv|last1=Bode|first1=Jan|last2=Kühl|first2=Niklas|last3=Kreuzberger|first3=Dominik|last4=Hirschl|first4=Sebastian|last5=Holtmann|first5=Carsten|author-link=|date=2023-05-04|title=Data Mesh: Motivational Factors, Challenges, and Best Practices|eprint=2302.01713v2|class=cs.AI}}</ref><ref name="Vestues2022">{{cite book |last1=Vestues |first1=Kathrine |last2=Hanssen |first2=Geir Kjetil |last3=Mikalsen |first3=Marius |last4=Buan |first4=Thor Aleksander |last5=Conboy |first5=Kieran |title=Agile Processes in Software Engineering and Extreme Programming |year=2022 |chapter=Agile Data Management in NAV: A Case Study |series=Lecture Notes in Business Information Processing 445 LNBIP |volume=445 |pages=220–235 |publisher=Springer |doi=10.1007/978-3-031-08169-9_14 |isbn=978-3-031-08168-2 }}</ref><ref name="Joshi2021">{{cite conference|last1=Joshi|first1=Divya|last2=Pratik|first2=Sheetal|last3=Rao|first3=Madhu Podila|title=Datagovernanceindata mesh infrastructures: The Saxo bank case study|book-title=Proceedings of the International Conference on Electronic Business (ICEB)|volume=21|year=2021|pages=599–604}}</ref>
; 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.<ref name="Bode2023">{{cite arXiv|last1=Bode|first1=Jan|last2=Kühl|first2=Niklas|last3=Kreuzberger|first3=Dominik|last4=Hirschl|first4=Sebastian|last5=Holtmann|first5=Carsten|author-link=|date=2023-05-04|title=Data Mesh: Motivational Factors, Challenges, and Best Practices|eprint=2302.01713v2|class=cs.AI}}</ref><ref name="Vestues2022">{{cite book |last1=Vestues |first1=Kathrine |last2=Hanssen |first2=Geir Kjetil |last3=Mikalsen |first3=Marius |last4=Buan |first4=Thor Aleksander |last5=Conboy |first5=Kieran |title=Agile Processes in Software Engineering and Extreme Programming |year=2022 |chapter=Agile Data Management in NAV: A Case Study |series=Lecture Notes in Business Information Processing 445 LNBIP |volume=445 |pages=220–235 |publisher=Springer |doi=10.1007/978-3-031-08169-9_14 |isbn=978-3-031-08168-2 }}</ref><ref name="Joshi2021">{{cite conference|last1=Joshi|first1=Divya|last2=Pratik|first2=Sheetal|last3=Rao|first3=Madhu Podila|title=Datagovernanceindata mesh infrastructures: The Saxo bank case study|book-title=Proceedings of the International Conference on Electronic Business (ICEB)|volume=21|year=2021|pages=599–604}}</ref>
;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.<ref name="Bode2023"/><ref name="Vestues2022"/>
;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.<ref name="Bode2023"/><ref name="Vestues2022"/>
Line 32: Line 32:


== Community ==
== Community ==
[[Scott Hirleman]] has started a data mesh community that contains over 7,500 people in their Slack channel.<ref>{{Cite web |title= The Global Home for Data Mesh |url=https://datameshlearning.com/ |access-date=2022-04-24 |website= The Global Home for Data Mesh |language=en-US}}</ref>
[[Scott Hileman|Scott Hirleman]] has started a data mesh community that contains over 7,500 people in their Slack channel.<ref>{{Cite web |title= The Global Home for Data Mesh |url=https://datameshlearning.com/ |access-date=2022-04-24 |website= The Global Home for Data Mesh |language=en-US}}</ref>


== See also ==
== See also ==

Latest revision as of 12:19, 1 June 2024

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[1] and Manuel Pais’ and Matthew Skelton’s theory of team topologies.[2] Data mesh mainly concerns itself with the data itself, taking the data lake and the pipelines as a secondary concern. [3] The main proposition is scaling analytical data by domain-oriented decentralization.[4] 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.[5] 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.[6]

History[edit]

The term data mesh was first defined by Zhamak Dehghani in 2019[7] while she was working as a principal consultant at the technology company Thoughtworks.[8][9] 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.[10][11] Data meshes have been implemented by companies such as Zalando,[12] Netflix,[13] Intuit,[14] VistaPrint, PayPal[15] and others.

In 2022, Dehghani left Thoughtworks to found Nextdata Technologies to focus on decentralized data.[16]

Principles[edit]

Data mesh is based on four core principles:[17]

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.[19] In other words, the data should be treated as a product that is ready to use and reliable.[20]

In practice[edit]

After its introduction in 2019[7] multiple companies started to implement a data mesh[12][14][15] 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.[21][22][23]
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.[21][22]
C3. Comprehension
Research has shown a severe lack of comprehension for the data mesh paradigm among employees of companies implementing a data mesh.[21]
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.[21][24]
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.[21]
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).[21]

Community[edit]

Scott Hirleman has started a data mesh community that contains over 7,500 people in their Slack channel.[25]

See also[edit]

References[edit]

  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. ^ a b "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. ^ a b 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. ^ a b Baker, Tristan (2021-02-22). "Intuit's Data Mesh Strategy". Intuit Engineering. Retrieved 2022-04-29.
  15. ^ a b "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. ^ a b c d e f Bode, Jan; Kühl, Niklas; Kreuzberger, Dominik; Hirschl, Sebastian; Holtmann, Carsten (2023-05-04). "Data Mesh: Motivational Factors, Challenges, and Best Practices". arXiv:2302.01713v2 [cs.AI].
  22. ^ a b 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.
  23. ^ Joshi, Divya; Pratik, Sheetal; Rao, Madhu Podila (2021). "Datagovernanceindata mesh infrastructures: The Saxo bank case study". Proceedings of the International Conference on Electronic Business (ICEB). Vol. 21. pp. 599–604.
  24. ^ 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.
  25. ^ "The Global Home for Data Mesh". The Global Home for Data Mesh. Retrieved 2022-04-24.
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