With Unity Catalog, organizations can seamlessly govern both structured and unstructured data in any format, as well as machine learning models, notebooks, dashboards and files across any cloud or platform.
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tech12025-12-30
The article discusses the author's approach to structuring data pipelines by integrating the medallion architecture, Kimball dimensional modeling, and semantic layers. It emphasizes the importance of defining clear roles and outputs for each layer—Bronze, Silver, and Gold—to cater to different user needs. The author argues for making the semantic layer a first-class priority in data architecture, highlighting its role in providing governed metrics for self-service analytics. The article concludes with a concrete example of how marketing attribution data flows through this architecture.
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tech12026-02-02
The article discusses the introduction of catalog-managed tables in Delta Lake 4.1.0, which shift the management of table access and metadata from the filesystem to a catalog-centric model. This change aims to simplify table discovery, enhance governance, and improve performance by allowing clients to reference tables by name rather than by path. The article also highlights the challenges faced with filesystem-managed tables and how catalog-managed tables address these issues, paving the way for a more interoperable and efficient data ecosystem.
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product2026-03-04
The article discusses the challenges organizations face in finding and verifying data across analytics and AI workflows. It introduces Databricks' new Discover experience, which integrates business context and trust into the Unity Catalog, allowing users to find and access trusted data and AI assets more efficiently. The article highlights the importance of domains, intelligent curation, and governed access in facilitating a unified discovery experience that enhances user confidence and reduces bottlenecks in data access.
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product2025-05-17
The article discusses the advancements in text-to-SQL capabilities using Google's Gemini models, which allow users to generate SQL queries from natural language prompts. It highlights the challenges faced in understanding user intent, providing business-specific context, and the limitations of large language models in generating precise SQL. Various techniques to improve text-to-SQL performance are explored, including intelligent retrieval of data, disambiguation methods, and validation processes. The article serves as an introduction to a series on enhancing text-to-SQL solutions within Google Cloud products.
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project2025-07-08
i like this statement "We're not just building technology; we're building expertise." :)
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project2024-12-10
modern data platform architecture based on Databrick tech stack.
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project2024-12-03
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