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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 organization 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.
In this talk, Zhamak will introduce the principles underpinning Data Mesh and architecture.
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