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Join the largest community of 8000+ data leaders implementing data mesh. Learn about data mesh from our library of resources. Connect with people at all stages of their data mesh journey.

What is Data Mesh?

Data should be owned by those who know it best. Data mesh is an approach that enables organizations to become data-driven by changing the way they organize their teams and their data architecture. It encourages organizations to treat data as product, not a byproduct; and to decentralize data ownership and data architecture. Data mesh is not a technology, it’s an approach to being data-driven.

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Data Mesh Learning

We maintain a library of resources to help jumpstart or advance your data mesh journey.

Zhamak Dehghani on Data Mesh, Domain-Oriented Data, and Building Data Platforms

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In this podcast, Daniel Bryant sat down with Zhamak Dehghani, principal consultant, member of technical advisory board, and portfolio director at ThoughtWorks. Topics discussed included: the motivations for becoming a data-driven organization; the challenges of adapting legacy data platforms and ETL jobs; and how to design and build the next generation of data platforms using ideas from domain-driven design and product thinking, and modern platform principles such as self-service workflows.

Data Mesh: Delivering Data-Driven Value at Scale

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We're at an inflection point in data, where our data management solutions no longer match the complexity of organizations, the proliferation of data sources, and the scope of our aspirations to get value from data with AI and analytics. In this practical book, author Zhamak Dehghani introduces data mesh, a decentralized sociotechnical paradigm drawn from modern distributed architecture that provides a new approach to sourcing, sharing, accessing, and managing analytical data at scale. Dehghani guides practitioners, architects, technical leaders, and decision makers on their journey from traditional big data architecture to a distributed and multidimensional approach to analytical data management. Data mesh treats data as a product, considers domains as a primary concern, applies platform thinking to create self-serve data infrastructure, and introduces a federated computational model of data governance.

Data Mesh Principles and Logical Architecture

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Data Mesh Principles and Logical Architecture

Data Meshes: Big Data Architecture Becoming Distributed, Declarative and Domain Oriented

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Beyond The Data Lake was Director of Emerging Technologies at ThoughtWorks, Zhamak Dehghani's 2017 paper that was a guiding light for Sam Ramji at another point in his career. Listen to how a Data Mesh allows the composition of multi-model data across an organization and beyond.

How to Move Beyond a Monolithic Data Lake to a Distributed Data Mesh

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Many enterprises are investing in their next generation data lake, with the hope of democratizing data at scale to provide business insights and ultimately make automated intelligent decisions. Data platforms based on the data lake architecture have common failure modes that lead to unfulfilled promises at scale. To address these failure modes we need to shift from the centralized paradigm of a lake, or its predecessor data warehouse. We need to shift to a paradigm that draws from modern distributed architecture: considering domains as the first class concern, applying platform thinking to create self-serve data infrastructure, and treating data as a product.

Data Mesh: The Four Principles of the Distributed Architecture

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A data mesh is a decentralised architecture devised by Zhamak Dehghani, director of Next Tech Incubation, principal consultant at Thoughtworks and a member of its technology Advisory Board.
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