For over half a century organizations have assumed that data is an asset to collect more of, and data must be centralized to be useful. These assumptions have led to centralized and monolithic architectures, such as data warehousing and data lake, that limit organizations to innovate with data at scale. Data Mesh is an alternative architecture and organizational structure for managing analytical data. Its objective is enabling access to high quality data for analytical and machine learning use cases - at scale. It's an approach that shifts the data culture, technology and architecture - from centralized collection and ownership of data to domain-oriented connection and ownership of data - from data as an asset to data as a product - from proprietary big platforms to an ecosystem of self-serve data infrastructure with open protocols - from top-down manual data governance to a federated computational one. This is a well-rounded introductory talk to Data Mesh. Why you might need one, what it is and how to get started with implementing it.
Zhamak Dehghani works with ThoughtWorks as the director of emerging technologies in North America, with a focus on distributed systems and data architecture, and a deep passion for decentralized technology solutions. She founded the concept of Data Mesh in 2018, a shift in analytical data management toward data decentralization, and since has been implementing the concept and evangelizing it in the wider industry. Zhamak serves on multiple tech advisory boards including ThoughtWorks and contributes to the creation of ThoughtWorks Technology Radar. Zhamak has worked as a technologist for over 20 years and has contributed to multiple patents in distributed computing communications, as well as embedded device technologies.
