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The most meaningful development in contemporary data architecture isn’t the growing interest in the concepts of the data mesh and the data fabric. Instead, it’s the potential for convergence of these two architectural approaches into a single architecture that supports both decentralization and centralization of integrated data, local data ownership, universal accessibility, and top-down and bottom-up implementation methods.
In reality, data meshes and data fabrics are more similar than they are morocco rcs data different. Rather than opposing one another, I would argue that they’re complementary constructions for making data available across (and between) organizations. When properly implemented with knowledge graph technologies, they become a powerful approach for devising reusable, integrated data products that can span both business domains and the enterprise as a whole.
Combining Top-Down and Bottom-Up Methodologies
What are the core principles of a data mesh and data fabric? The data mesh concept is simply a bottom-up philosophy for assigning responsibility for data to specific business units or business domain expertise groups while de-emphasizing centralized infrastructure like data warehouses. A data fabric is a top-down, user self-service-driven methodology for integrating data from many parts of an organization. Typically it assigns responsibility for contributing datasets closer to where the data is produced; it is also purported to utilize artificial intelligence (AI) using metadata to automate the discovery and integration of data to achieve a centralized version of the truth, an approach that is becoming increasingly viable with the rise of data description and integration solutions based on generative AI (genAI).