![[Review] Data Mesh: Delivering Data-Driven Value at Scale (Zhamak Dehghani) Summarized.](https://episodes.castos.com/660078c6833215-59505987/images/2663267/c1a-085k3-okkw8mkjc9w6-ceh5hi.jpg)
Show Notes
Data Mesh: Delivering Data-Driven Value at Scale (Zhamak Dehghani)
- Amazon USA Store: https://www.amazon.com/dp/1492092398?tag=9natree-20
- Amazon Worldwide Store: https://global.buys.trade/Data-Mesh%3A-Delivering-Data-Driven-Value-at-Scale-Zhamak-Dehghani.html
- Apple Books: https://books.apple.com/us/audiobook/data-mesh-delivering-data-driven-value-at-scale/id1677239605?itsct=books_box_link&itscg=30200&ls=1&at=1001l3bAw&ct=9natree
- eBay: https://www.ebay.com/sch/i.html?_nkw=Data+Mesh+Delivering+Data+Driven+Value+at+Scale+Zhamak+Dehghani+&mkcid=1&mkrid=711-53200-19255-0&siteid=0&campid=5339060787&customid=9natree&toolid=10001&mkevt=1
- Read more: https://english.9natree.com/read/1492092398/
#Domainownership #Dataasaproduct #Selfservedataplatform #Federatedcomputationalgovernance #Interoperabledataproducts #DataMesh
Data Mesh: Delivering Data-Driven Value at Scale is a practical technology and organizational design book by Zhamak Dehghani, published by O Reilly. It addresses a recurring problem in large organizations: centralized data warehouses and lakes often become bottlenecks as the number of data sources, business domains, analytical uses, and machine learning needs grows. Dehghani argues that this is not merely a tooling problem. It reflects a mismatch between centralized data ownership and the distributed nature of modern businesses. The book sets out the rationale, principles, architectural implications, and adoption considerations for data mesh, a decentralized sociotechnical approach to analytical data. Its central proposition is that business domains should create and maintain trustworthy, discoverable, interoperable data products, supported by a shared self-service platform and coordinated through federated computational governance. Rather than offering a ready-made vendor blueprint, the book provides a conceptual foundation for architects, technical leaders, and decision-makers who need to assess whether and how this demanding model could improve data-driven work at organizational scale.
- Amazon USA Store: https://www.amazon.com/dp/1492092398?tag=9natree-20
- Amazon Worldwide Store: https://global.buys.trade/Data-Mesh%3A-Delivering-Data-Driven-Value-at-Scale-Zhamak-Dehghani.html
- Apple Books: https://books.apple.com/us/audiobook/data-mesh-delivering-data-driven-value-at-scale/id1677239605?itsct=books_box_link&itscg=30200&ls=1&at=1001l3bAw&ct=9natree
- eBay: https://www.ebay.com/sch/i.html?_nkw=Data+Mesh+Delivering+Data+Driven+Value+at+Scale+Zhamak+Dehghani+&mkcid=1&mkrid=711-53200-19255-0&siteid=0&campid=5339060787&customid=9natree&toolid=10001&mkevt=1
- Read more: https://english.9natree.com/read/1492092398/
#Domainownership #Dataasaproduct #Selfservedataplatform #Federatedcomputationalgovernance #Interoperabledataproducts #DataMesh
Data Mesh: Delivering Data-Driven Value at Scale is a practical technology and organizational design book by Zhamak Dehghani, published by O Reilly. It addresses a recurring problem in large organizations: centralized data warehouses and lakes often become bottlenecks as the number of data sources, business domains, analytical uses, and machine learning needs grows. Dehghani argues that this is not merely a tooling problem. It reflects a mismatch between centralized data ownership and the distributed nature of modern businesses. The book sets out the rationale, principles, architectural implications, and adoption considerations for data mesh, a decentralized sociotechnical approach to analytical data. Its central proposition is that business domains should create and maintain trustworthy, discoverable, interoperable data products, supported by a shared self-service platform and coordinated through federated computational governance. Rather than offering a ready-made vendor blueprint, the book provides a conceptual foundation for architects, technical leaders, and decision-makers who need to assess whether and how this demanding model could improve data-driven work at organizational scale.
