Discover the latest advancements in AI/BI through the October 2025 Roundup Databricks blog. Key highlights include: - Embedded dashboards designed for external (non-Databricks) users - A smarter, faster Genie featuring new APIs and enhanced benchmarking - Tags and certification for governed, discoverable AI/BI content - New visualizations, including funnel and waterfall charts - And more! https://lnkd.in/eKKPpBwQ Stay informed on how these updates can enhance your data experience. Richard Tomlinson #Databricks #DatabricksMVP
Databricks October 2025 Roundup: AI/BI Advancements
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We're thrilled to be a launch partner for the groundbreaking new Snowflake Intelligence! 🚀 Snowflake Intelligence gives every user the ability to ask complex questions about their business in natural language and puts insights at every employees fingertips. 🦾 Elementum orchestrates its agentic functions to execute business processes for measurable business impact. 📎 Read more on this in our new blog: https://lnkd.in/gQcgP4Hh #SnowflakeIntelligence #EnterpriseAI #DataCloud #LaunchPartner #IntelligentAgents ❄️ Snowflake + Elementum AI
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🚀 AI Agents are redefining Enterprise Data Intelligence. Just completed the AI Agents course from Databricks and it reinforced the fact that the future of analytics isn’t just insight generation, it’s autonomous intelligence. Databricks is pioneering a new layer where AI Agents can interpret Lakehouse data, reason over business context, and take actions , transforming static data platforms into dynamic systems of AI-Driven Data intelligence. With innovations like Agent Bricks, Databricks is bridging the gap between data pipelines and decision intelligence. It’s not just about storing or analysing data anymore , it’s about creating autonomous, data-aware agents that can reason, act, and optimise in real time. The future of analytics isn’t static dashboards , it’s agents that think with your data The question is , how ready is our data stack for it? #Databricks #AIAgents #DataIntelligence #LakehouseAI #EnterpriseAI #GenerativeAI
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The future of analytics, and AI runs on consistent metrics. Announced at #dbtCoalesce: MetricFlow is now open source (Apache 2.0). It’s the engine behind the dbt Semantic Layer, and it ensures every tool in your stack—from BI dashboards to LLMs—uses the same, governed definitions. This move opens MetricFlow to the entire data ecosystem: co-maintained with Snowflake and Salesforce, aligned with OSI standards, and ready for anyone to build on. One open standard. Trusted metrics. Better AI. Details https://lnkd.in/eXCXUZnt
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When a Fortune 50 logistics and transportation company wanted to enhance business visibility and actionability to ensure business continuity and improve cost and margin decisions, our team of experts stepped in. We led the comprehensive modernization of the client’s data, AI, and analytics platforms by leveraging Databricks to provide AI-powered decision-making, real-time intelligence, and better cost savings. More details of the successful engagement: https://lnkd.in/gCM6B5_E #Databricks #platformmodernization #sastodatabricksmigration #Infogain #analyticsplatform #successstory
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With real-time data becoming table stakes, how are data teams connecting analytics engines to operational systems at zero-latency? At Foundations 2025, Databricks leaders shared that tomorrow’s analytics hinges on federated access, low-latency connectivity, and AI‑driven intelligence. Dive into how CData Software + Databricks power that bridge: https://bit.ly/43v0TqM #CData #CDataFoundations #Databricks #AI #DataEngineering
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Having access to multiple models in one platform is powerful. Being able to systematically evaluate them on your data, with your quality criteria, is what makes that power actionable. Generic benchmarks won't tell you which model handles your specific use case best. You need to evaluate on realistic scenarios, define what "good" means for your application, and compare models at scale. We built a full evaluation workflow using MLflow on Databricks to show how this works in practice. The key steps: - Build diverse test cases: not just happy paths, but edge cases, ambiguous situations, and multi-factor decisions - Define complementary judges: trace-aware judges that verify decisions are grounded in actual tool outputs, plus template judges that assess reasoning quality - Add human feedback: review judge assessments and provide corrections to align them with your standards - Align your judges with human feedback: automatically improve judge instructions based on the patterns in your annotations - Compare models systematically: run multiple models through the same evaluation pipeline with aligned judges The technical post walks through this using Casper's Kitchens, a realistic ghost kitchen environment running on Databricks. It's a complaint triage agent that queries Unity Catalog functions, analyzes customer issues, and recommends credits or escalations. This evaluation pattern works for any tool-calling agent: code assistants, retrieval systems, domain-specific workflows. If your agent makes decisions based on data it retrieves, you need judges that can verify those decisions are grounded in what the tools actually returned. 📖 Read the full walkthrough: https://lnkd.in/gkKbK5QR 🧪 Try the make_judge functionality in MLflow to build your own trace-aware and template-based judges 🛠️ Explore Casper's Kitchens: https://lnkd.in/gbFpSkAW 📅 Join us 11/11 for "The Future of AI: Build Agents that Work" with Sam Altman and Ali Ghodsi: https://lnkd.in/gp2Mnutv #AI #Databricks #agents #MLflow
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Ataccama is defining what trust looks like in the AI era. 🔐 Together, we built an enterprise-grade MCP server that powers the industry’s first cross-platform AI Trust Layer, bringing Ataccama’s data quality and lineage intelligence directly into AI assistants like Claude, Power BI, and Snowflake Cortex. Read the full case study 👉 https://lnkd.in/eZWCqSGE
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“Cloud-Native and Multi-Cloud BI aren’t just about where data lives—they’re about how freedom breathes. True intelligence doesn’t belong to one platform; it flows, adapts, and evolves wherever insight finds purpose.” In an era of constant change, resilience is no longer built by control, but by flexibility. The future of analytics lies in architectures that transcend boundaries—where agility, interoperability, and truth move as freely as the clouds themselves. #CloudNative #MultiCloud #BusinessIntelligence #DataArchitecture #Analytics #DigitalTransformation #DataStrategy #DataDriven #AI #EnterpriseArchitecture
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The future of analytics, and AI runs on consistent metrics. Announced at #dbtCoalesce: MetricFlow is now open source (Apache 2.0). It’s the engine behind the dbt Semantic Layer, and it ensures every tool in your stack—from BI dashboards to LLMs—uses the same, governed definitions. This move opens MetricFlow to the entire data ecosystem: co-maintained with Snowflake and Salesforce, aligned with OSI standards, and ready for anyone to build on. One open standard. Trusted metrics. Better AI. Details https://lnkd.in/gxvB9_NE
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Just a year ago, Agentic AI was largely experimental, requiring development of custom infrastructure. Now, Snowflake Intelligence brings accessible Agentic AI to the Enterprise.
Today marks a major milestone in our journey to bring agentic AI to every enterprise: Snowflake Intelligence is now generally available! Snowflake Intelligence is more than an AI assistant. It’s an enterprise intelligence agent that empowers every employee to ask complex questions in natural language and get instant, actionable insights. It connects across all your data so you can move beyond dashboards and uncover the “why” behind the what. We can’t wait to see what you build with Snowflake Intelligence. We’ve seen incredible momentum to-date, with over 1,000 of our customers using Snowflake Intelligence to deploy 15,000+ AI agents across their businesses, transforming how teams at organizations like Cisco, Toyota Motor Europe, Fanatics, and Wolfspeed operate and make decisions. #SnowflakeBUILD Read more in Snowflake’s blog post: https://lnkd.in/ga3j5Wpu
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