Yue Deng

Bring dbt's Governance to Your Snowflake Semantic Views with the New dbt_semantic_view Package! It’s incredibly rewarding to see this project finally come to life! This package is a foundational step toward true governance for the centralized semantic layer, as it extends dbt's strengths in version control, testing, and CI/CD directly to your metrics. Huge thanks to the phenomenal team that brought this to life! Snowflake: Tech Lead Kevin Wang, PMs Josh Klahr and Karan Asher, EM Vlad Bunescu, Data Scientist Wendy Grus, Marketing Sarah Nangle & Nick El-Rayess, MBA, and the entire Semantic View team. A special shoutout to Amy Chen, Courtney Holcomb, and the dbt Labs team for their professional and generous partnership! Check out the announcement below and give the package a spin! See more: https://lnkd.in/eZqiwT-g #Snowflake #dbt #DataGovernance #AnalyticsEngineering #SemanticView

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Why MCP Matters for the Future of AI As LLMs move from demos to real-world systems, the biggest challenge isn’t model capability—it’s context, action, and integration at scale. In this blog, I break down Model Context Protocol (MCP): • Why prompt-based integrations don’t scale • How MCP solves the M×N integration problem • Why MCP is emerging as the operating system for AI agents If you’re building agentic systems, RAG pipelines, or enterprise AI, MCP is a concept worth understanding early. 🔗 Read the blog here: https://lnkd.in/gJpcAHdt #AI #LLM #MCP #AgenticAI #Architecture #FutureOfAI

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We’re learning a lot at Snowflake, very quickly, about what it actually takes to deliver high quality, high-performance agentic analytics in the real world. Rajhans Samdani and I collaborated to write this latest post, which captures some of those learnings, especially around the emerging “context layer” and why it is becoming foundational for trustworthy data agents. https://lnkd.in/g-8TMcmD What’s been most interesting is how consistently the same themes show up across customer engagements. Whether it’s Semantic Views, Cortex Analyst, our Agent Framework, or Snowflake Intelligence, the same pattern keeps reinforcing itself. You can’t get to reliable, scalable outcomes without a strong, well-defined layer of context that connects data, business meaning, and user intent. This is one of those moments where the pace of change is hard to overstate. The space is evolving quickly, and we’re seeing ideas move from early experimentation into real customer impact in a matter of weeks. Being able to take what we’re learning directly from customers, feed that back into the platform, and then see it improve results is what makes this work so exciting right now. If you’re thinking about how to build or scale agentic analytics, I hope you’ll find this perspective useful.

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** Big update on the roadmap for Meta's MTIA roadmap (our Meta-designed AI chips) ** We just shared today a big update on the MTIA (Meta Training & Inference Accelerator) roadmap. The main takeaway is we're accelerating the whole set of programs to get rapid iterations on chips (given how fast the model needs are evolving). 👉 Overall blog is here: https://lnkd.in/gaHuxUfg For the networking aficionados, you can see a whole lot of networking that we've been working on inside MTIA - we're excited to finally be able talk about it! 👉 The scale-up and scale-out networks we've already talked about. It's sort of buried in there, but we are already running distributed training clusters with MTIA 300 for core ranking & recommendation (even as we focus future generations on Inference). 👉 The communication library (HCCL) that is purpose-built to take advantage of networking-specific capabilities we've built into the chips to offload the main compute. 👉 The in-built support for fused compute/communication kernels to minimize latency (key for both training and inference workloads). Super-proud of the whole MTIA team!

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What are the most underrated Databricks capabilities? Watch my conversation with Shiv Trisal (Global Industrials GTM @ Databricks) on the topic below. For the full, practical, no-fluff discussion on real-world data + AI: https://lnkd.in/gMPsFHbY

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