In the era of generative AI and large language models (LLMs), the quality and accessibility of data have become the primary differentiators for enterprise success. However, many organizations remain trapped in the architectural paradigms of the past — centralized data lakes and warehouses that create massive bottlenecks, high latency, and “data swamps.”
Enter the Data Mesh. Originally proposed by Zhamak Dehghani, Data Mesh is a sociotechnical approach to sharing, accessing, and managing analytical data in complex environments. When paired with the scaling capabilities of Google BigQuery, it creates a foundation for “AI Excellence,” where data is treated as a first-class product, ready for consumption by machine learning models and business units alike.
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