Gabriel Gejman

If you want to see some of the key features of Databricks' Unity Catalog in action (like ABAC, Lineage, Lakehouse Federation, etc) highly encourage you to check out my video with Paul Roome: https://lnkd.in/e9hw3rmj

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This is a neat trick. It is obvious that materialized views improve performance of the query when used. What is not obvious is that you DO NOT need to reference materialized view in your query to benefit the performance improvement (query optimizer can do it for you).

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On November 24, 2025, Snowflake announced the acquisition of metadata management platform Select Star, further enhancing its data cataloging and governance capabilities. From a platform and team management perspective, this acquisition has several key implications for the enterprise and data platform team: Improved Platform Visibility and Governance: Integrating Select Star makes data assets easier to track and manage, enabling platform teams to coordinate projects across engineering, data, and security more efficiently. Optimized Cross-Team Collaboration: Enhanced data cataloging and governance capabilities facilitate information sharing and collaboration among engineering, data, and security teams, supporting the smooth progress of key milestones. Support for Business and Technology Project Implementation: More robust metadata management capabilities allow the platform to respond quickly to changes in business or technical needs, improving the stability and reliability of platform services.

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At Snowflake, we act as #CustomerZero when it comes to the use of our own products -- for one, because they're that good 😊 , and also so we can accelerate product innovation and reinforce data trust prior to public release. We want our products to be the best they can be, so we test and provide feedback to our product teams when we identify even the smallest opportunity for improvement. I spoke about this and how to overcome legacy data silos, accelerate enterprise AI adoption, and build trustworthy systems with SiliconANGLE & theCUBE at #SnowflakeSummit. Check it out: https://lnkd.in/gKWm6s_s

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Are Your Data Leaders Thinking… or Just Connecting? Here’s an uncomfortable truth. Many data teams spend most of their time not analyzing… not predicting… not innovating… But connecting systems. Fixing flows. Maintaining pipelines. That’s not a talent problem. That’s an architecture problem. When ingestion, governance, and analytics live in different tools, Your smartest engineers end up stitching instead of strategizing. That ratio is broken. Modern platforms like Microsoft Fabric shift the balance: • Built-in ingestion • Built-in governance • Built-in analytics Less system stitching. More business thinking. Because scaling insight shouldn’t require scaling headcount. 📩 datafabric@quadranttechnologies.com Write to us for a quick walkthrough or POC discussion. And if you’re heading to FabCon Atlanta (March 18–20), let’s connect in person; see you there. Microsoft | Quadrant Technologies | Ram Paluri, MBA | Vamshi Reddy | Bhaskar Gangipamula | James Kass | Prakash Nagarajan | Lavina DSilva | Mithun P N | Dr. Madhavi Gundavajyala | Siva Kanuru | Sivani Pamidi | Varsha Panguluri | Ajay kumar Erukulla | #Microsoft #DataStrategy #MicrosoftFabric #CIO #CDO #ModernData #FabCon

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Databricks SQL vs. Snowflake: Up to 5× Faster & 4× Lower Cost for ETL. Liberate your #datawarehouse from proprietary tariffs. Welcome to the world of open, unified, and unmatched TCO.

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I am pretty excited about what's going on at Snowflake particularly around automating the front end of data science and analytics. Check out my latest short on the topic below.

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Agent Learning from Human Feedback (ALHF) in Action A Databricks Case Study Databricks is pushing the frontier of Agentic AI by enabling its Knowledge Assistant to learn and improve through human feedback loops. By leveraging ALHF, Databricks created an agent that continuously learns from real user interactions refining responses, grounding answers in enterprise context, and evolving autonomously over time. Key Takeaways: Human-in-the-loop reinforcement makes agents more reliable and adaptive. Grounded learning boosts accuracy and trust in enterprise AI use cases. Feedback-based training ensures alignment with user intent and enterprise knowledge. A strong reminder: The future of Autonomous Agents isn’t just about LLMs it’s about learning responsibly, evolving contextually, and scaling enterprise value. #AgenticAI #ALHF #HumanFeedback #Databricks #EnterpriseAI #LLMOps #AutonomousAgents #AIinEnterprise #GenAI #AIProductDesign

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"The next wave of AI success is going to be completely dependent on structured data... Data is the mature, logical, boring middle, but if you want an honest assessment of where all the smart AI is going, look to those mature, logical, boring leaders pushing palatable hypergrowth." https://lnkd.in/gprqvzpz

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