Ryan Chynoweth

Claude, ChatGPT, Snowflake Intelligence, Cortex Analyst, Databricks Genie, Zenlytic, TextQL. Think about any AI agent you connect to your data. They all get questions from business users like: “What was the average number of daily active users in the last quarter?” These AI agent connects to Euno’s MCP server to retrieve metadata. That’s when Euno gives these agents the missing context they need to understand your data. ** Just a reminder that Euno is a metadata platform so we only access your metadata ** Euno runs a semantic search and looks for dashboards that use DAU (which is semantically equivalent to Daily Active Users). It filters for certified dashboards using the trust signals you’ve already set in Euno. In this example Euno finds two candidates: 1. DAU used in the User Engagement dashboards 2. DAU_NEW used in the Product Statistics dashboard Both dashboards are certified. But Euno also shows how often each has been used in the past 30 days, along with the owner or creator. With that information, the business user can confidently choose the right one and the AI agent can deliver the correct answer. The same applies to tools like Cursor. Let’s say you’re an engineer and ask: “Can you find me the Tableau dashboard for Customer Success?” Cursor will reach out to Euno’s AI assistant to get that BI context because Cursor is focused on dbt development and doesn’t have access to that layer. You can even go further and ask which dbt models power that dashboard and how those models are calculated. Euno feeds Cursor with that real-time context as you build. It’s not just for business users. It’s for developers too.

25

5 Comments

Build with GPT-5 on Databricks using AI Gateway OpenAI just dropped GPT-5—and you can use it immediately on Databricks. This post covers how to get started in minutes, securely govern usage, and run GPT-5 (and gpt-oss) at scale with built-in monitoring, cost controls, and safety filters.

4

Snowflake just released Iceberg V3 support into public preview today. A few highlights. Nanosecond timestamp precision – Iceberg tables can now match the same precision as native Snowflake tables. Row lineage metadata – better visibility into how individual rows evolve across updates and deletes. VARIANT support – yay 🙂 Deletion vectors – enabled by default in V3. Improves performance from reducing the overhead of managing many delete files. Can mean faster ingestion and transformation! Nice to see Iceberg tables continuing to close the gap with native tables in Snowflake.

114

2 Comments

Databricks Workspace Hygiene is a crucial activity to ensure you are only paying for the 💰value💰 that you get from the platform. https://lnkd.in/g5spJ7R7 Check out my recent blog! Model Serving Hygiene: Optimizing "Steady-State" Endpoint Usage #databricks #cost #modelserving #ai #letsgetthisbread

21

2 Comments

As someone operating in both Snowflake and Databricks environments, I’m energized by Snowflake’s vision shared at the summit last week — and just as excited to see what Databricks unveils at their Summit this week. For those of us bridging structured + unstructured, BI + ML, this cross-platform momentum is real and valuable. 🚀 Reflections from #SnowflakeSummit 2025 The big picture laid out by Snowflake’s leadership was bold and forward-looking: a unified platform where governed data, agentic AI, and transactional + analytical apps come together seamlessly. Sridhar Ramaswamy’s keynote along with Sam Altman framed it best: “There’s no AI strategy without a data strategy.” A truism. Key takeaways that stood out to me: 🤖Agentic AI in action Snowflake Intelligence and Cortex Agents bring a new paradigm—AI agents that retrieve, reason, and act—all within Snowflake’s secure data perimeter. 🧠AI for everyone Tools like AISQL and the Data Science Agent lower the barrier to insight. Prompt your data like you prompt ChatGPT—no Python required. ⚙️Smarter infrastructure Adaptive Compute and Gen2 Warehouses optimize cost and performance automatically—bringing efficiency without complexity. 🧾Unified workloads With PostgreSQL stack coming soon, Snowflake is ready to handle OLTP and analytics on the same platform. 🔐Governance at the core Semantic modeling, masking, and secure data sharing are not afterthoughts—they’re foundational to the future of responsible AI. #SnowflakeSummit #Databricks #AIstrategy #DataCloud #AgenticAI #AISQL #ProductLeadership #DataPlatform #Governance #CortexAgents

109

4 Comments

Get to your insights without pulling your laptop out. Enterprise scale data intelligence in the palm of your hand. #databricks

4

You think you’ve migrated. But 80% of your business logic is still stuck in that Teradata box no one dares to touch. Now you have two sources of truth. Snowflake is live. Pipelines in products look slick. Cloud migration in progress. But here’s the uncomfortable truth Half your data is still stuck in Oracle, SAP, or that Teradata box no one dares to touch. You didn’t modernize. You just built a duct-taped mess hybrid trap, legacy + cloud Hybrid environments look harmless at first. But duplicating logic, moving everything with no clear priortization, is a dead game. Running duplicated systems is not goal. It's a phase. I’ve seen it across dozens of companies: → Two data teams are maintaining duplicate pipelines. → Two budgets paying to process the same data. → And one VP still asking, “Which number do we trust?” Every month you delay decommissioning legacy systems, you’re paying double. That’s your “hybrid tax.” And most execs don’t even know they’re paying it. This includes 1. Double Spend. Duplicated Licenses, compute, ETL jobs. You’re not saving by “phasing it out slowly.” 2. Compounded Complexity. Your engineers are maintaining SQL and shell scripts, dbt and old Informatica Engineers are babysitting legacy. That’s why your best people quit. 3. Broken Trust. When half your dashboards pull from the cloud and half from legacy, Nobody believes the data. Self-service, compliance reporting, AI become impossible. 4. Lost Speed & Innovation. You can’t run AI or scale insights when half your business logic still lives on an old system. This isn’t about technology. It’s about focus and ruthless simplification. 1. Quantify the Hybrid Tax. Audit total cost of ownership, storage, ETL, maintenance, labor. Show the CFO the overlap. 2. Decouple Before You Decommission. Inventory every report, pipeline, and dashboard touching legacy systems. Then kill them one by one, not “all at once.” Move only the highest impact that relevant now not 12-18 months. The goal isn’t “all in the cloud.” The goal is no dual dependencies. 3. Redesign for Reuse. Hybrid environments expose one ugly truth: Your data models are too brittle to scale, usless and 89% deleted. Lock in your business definitions (semantic layer). Build modular, reusable data products. Document everything. 4. Redefine “Done.” “Cloud migration complete” isn’t done. Replicating everything into the cloud is just duplication at scale. The goal isn’t to move all your tables. The goal is to translate what matters, retire what doesn’t, and prove ROI at each step. 5. Institutionalize the Reset. Every month, review how much of your data still touches legacy. Everything should be priortized based on impact x complexity from the data strategy What % of workloads still depend on legacy? What costs can we retire next? and so on Have seen companies depend on 3 cloud systems + legacy, everything just amplified the chaos.

49

5 Comments