Mike Galvin

Cross Posting #Databricks CEO Just Dropped The Most Honest Advice About The Future Of AI The next wave of AI success is going to be completely dependent on structured data. Maybe that’s a no-brainer to you. Maybe I’m telling you that the next time you take a drink of water, that water will be wet. They don’t say things like all software developers will be replaced by AI (the deadline for which has since passed) or all tech employees should become baristas and bartenders (the dream is over, nerds) or don’t worry, we’ve hammered out all the unintended consequences for letting users create their own AI At the same time, AI is, theoretically anyway, offering executives and leaders actionable insights, options, and answers. AI Is Motion, Data Is Experience AI is fire, while data is fuel. https://lnkd.in/gqcRq6vY

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Last week's Snowflake BUILD dropped ~40 new capabilities. They're all incredibly powerful and the pace is amazing... but can your team keep up? Snowflake’s innovation curve is now officially faster than most organizations’ ability to operationalize it. Field CTO Keith Belanger breaks down why Snowflake's innovation engine has officially outpaced manual processes—and what it takes to operationalize the AI Data Cloud without burning out your team. 📖 Read Keith's full take on how leading data teams are bridging the gap: https://lnkd.in/g4HmVvc2 Question for the community: What's the biggest operational challenge you're facing as Snowflake adds more capabilities?

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Excited to be partnering with Databricks and several of their customers as we build out both contract pods and permanent teams of top-tier Databricks professionals. What we’re seeing across the market is clear: The new wave of AI Ops / MLOps engineers is becoming one of the most in‑demand skill sets - experts who can deploy models into live environments, operationalize AI at scale, and fully leverage the power of the Databricks platform. Across multiple projects, this capability gap is growing fast. Are you seeing the same on your side? If AI model deployment, platform operations, or scaling your Databricks talent is becoming a bottleneck for your team or your customer projects, let’s connect. Always happy to share what we’re learning and help where I can. Barney Green Nathan Miller #Databricks #AIOps #MLOps #AIEngineering #DataEngineering

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What happens when you pair LangGraph’s flexibility with Snowflake’s secure, scalable compute? You get a seamless path to production-grade LLM applications. Fabian Hernandez, part of our Infostrux team, walks you through how LangGraph agents can now be executed entirely inside Snowflake using its new LLM function framework. It’s an exciting step forward for anyone building: -LLM agents -Retrieval-augmented generation pipelines -Conversational workflows directly tied to enterprise data This article is a must-read for teams operationalizing AI where it matters most—in the cloud, with governance built in. Explore it here: 👉 https://hubs.ly/Q03ykwlj0 #Snowflake #LLMFunctions #LangGraph #AIWorkflows #GenAI #EnterpriseAI #Infostrux

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Snowflake’s acquisition of Select Star marks a major shift in how enterprises think about data, moving beyond scale to focus on trust, usability, and context. Over the past few years, Snowflake has been quietly assembling this capability: Datometry for seamless legacy migration, Crunchy Data for native PostgreSQL workloads, and Neeva for advanced search and retrieval. With Select Star now adding unified metadata and context, Snowflake is building an end-to-end pipeline that doesn’t just store data but understands it and equips AI to interpret it confidently. Read on: https://okt.to/o082Cx Get in touch: Mansi Gupta, Anju Elcy Babu Kattakathu

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We've launched the dbt_semantic_view package, seamlessly integrating the Snowflake semantic view with dbt's disciplined workflow. This means you can now: - Centralize Metrics: Define key metric logic (FACTS, DIMENSIONS, METRICS) natively in Snowflake's semantic view for a single source of truth. - Enable CI/CD: Apply dbt's strengths (declarative YAML, testing, and version control) directly to your semantic layer definitions. - Ensure Governance: Automatically manage privileges and validate relationships to ensure every consumer, from BI tools to AI agents, uses trusted data. - Unify your governance framework and ensure consistent, high-performance metrics across your enterprise. Learn more and get started: https://lnkd.in/eZqiwT-g

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Palantir & Databricks: powering end-to-end data, analytics, and operational AI. Chad Wahlquist and Benjamin Abood discuss how the Data Intelligence Platform, Unity Catalog, Foundry, and AIP all seamlessly integrate to deliver next-generation outcomes. Full interview here: https://lnkd.in/eprQUapp

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