Introducing dbt MCP server: a remote, governed AI workflow tool

Can one model context protocol server safely connect GenAI to all your enterprise data? Find out: https://lnkd.in/dxGX4f8p GenAI apps need real-time, governed data because LLMs can’t reason about your business using static, public training data alone. Pairing an AI data layer with the Model Context Protocol (MCP) delivers trusted, real-time context into every prompt, at scale — through a single, unified MCP server that connects to all enterprise systems. Read our latest blog post to learn how. #MCP #AIReadyData #GenAI #DataProducts #K2view

Bringing real-time, trusted enterprise data into GenAI is the game-changer 🚀 Pairing K2view’s Platform with the Model Context Protocol (MCP) means LLMs don’t just predict — they truly understand your business context at scale. K2view #MCP #AIReadyData #GenAI #DataProducts #K2view #LLM

Can one model context protocol server safely connect GenAI to all your enterprise data? Find out: https://lnkd.in/dxGX4f8p GenAI apps need real-time, governed data because LLMs can’t reason about your business using static, public training data alone. Pairing an AI data layer with the Model Context Protocol (MCP) delivers trusted, real-time context into every prompt, at scale — through a single, unified MCP server that connects to all enterprise systems. Read our latest blog post to learn how. #MCP #AIReadyData #GenAI #DataProducts #K2view

What role do you think machine learning will play in personalizing data experiences for users? As the landscape of technology evolves, the integration of machine learning with platforms like Snowflake opens up incredible possibilities. The article highlights the concept of the Adaptive Cortex, which merges machine learning capabilities with data clouds, offering businesses insights that drive intelligent decision-making. This synergy not only streamlines operations but also enhances user engagement by tailoring interactions based on individual user behaviors and preferences. In the ever-changing environment of data science, understanding how these technologies coexist is crucial for all levels of developers. I'm eager to hear your thoughts and insights on how machine learning is transforming the way we approach data management and user experience. #DataScience #MachineLearning #Snowflake #Innovation #Community https://lnkd.in/gYwuBgcQ

A data lakehouse is a new, open data management architecture that combines the flexibility, cost-efficiency, and scale of data lakes with the data management and ACID transactions of data warehouses, enabling business intelligence (BI) and machine learning (ML) on all data.

We help companies build AI-ready data architectures that actually deliver measurable ROI. Our approach includes: 🗄️ Unified data systems: OLTP + analytics in a single platform ⏱️ Real-time pipelines for faster decision-making 🧠 Vector search & ML-ops fully integrated for future AI projects With our experts, your data systems are prepared for AI production, enabling faster deployment and reliable outcomes. 🔗 Learn more about building AI-ready infrastructure: https://lnkd.in/dPKgqfwq

💥 Think you can slap an AI model on your legacy data and call it transformation? Who are you, Einstein trying to run Excel on an air fryer? 🥸 😂 NVMD (www.nvmd.tech) shows that poor data quality alone drives an 87% failure rate in AI projects, costing U.S. businesses $3.1 trillion in lost productivity every year. Real-world receipts: ❌ Failure: IBM Watson Health spent $62 billion chasing AI on broken data foundations. It collapsed. ➡️ (here’s the facepalm: https://lnkd.in/e_4vJSr4) ✅ Success: Capital One invested $250 million in data infrastructure first. Model errors dropped 45% and ROI scaled across the enterprise. ➡️ (full story: https://lnkd.in/e_4vJSr4) Want proof? ❌ Failure: MIT found that 95% of AI pilots never make production. ➡️ (need my source?: https://lnkd.in/e746wnRu) ❌ Failure: Fivetran reports nearly half of enterprise AI projects stall due to poor data readiness. ➡️ (yep, it’s real: https://lnkd.in/eDWs5T2s) ✅ Success: TrendCandy/Reltio highlight fractured data as a killer. McDonald’s avoided it by spending years on data hygiene first, then rolled out AI smoothly at scale. ➡️ (proof’s in the fries: https://lnkd.in/eX-iqQRr) ✅ Success: TechRadar confirms, “Real AI success begins not with models, but with trusted, unified data foundations driving smarter, compliant decisions.” ➡️ (Here’s your sign: https://lnkd.in/eRQzTeYg ) The lesson is clear: build data infrastructure that’s AI-ready, not just AI-compatible. Governance, real-time access, unified oversight… these are not bureaucratic headaches, they’re your launchpad. So here’s your swagger move: stop chasing AI models and partner with NVMD (www.nvmd.tech) to fix the data plumbing first. Because shiny AI without solid infrastructure isn’t innovation, it’s just a $62 billion mistake waiting to happen. 👉 If your AI strategy feels clogged, it’s not the faucet, it’s the pipes. NVMD is the plumber your data has been waiting for. Reach out to me directly if you’re ready to stop pouring money into failed projects and start building AI that actually works.

Did you know you can now use Snowflake Cortex AISQL in the SELECT clause for dynamic tables? This new capability brings AI-powered insights directly into your pipelines, allowing you to automatically analyze data as it updates. For example, you can use an LLM function like AI_FILTER to classify customer reviews or survey responses as they're ingested. This means you can get instant sentiment analysis on new data without any extra steps. Learn more: https://lnkd.in/gQCTt2gA

This is wild. I am not sure if that works as a process but can’t clearly identify from my head right now why it shouldn’t. You can run quality checks and do modifications in later steps. This means declarative AI data transformation pipelines are as simple as writing a select statemen. No API integration needed. No separate scheduler needed. No orchestrator needed (dynamic tables determine the correct build order automatically). No separate lineage needed. No vendor for AI needed. No training for developers needed. No separate monitoring needed. All that you need is a CI pipeline and setup staging. But I got a feeling that Snowflake will automate this, too with the new workspace experience in the near future. Want to hear more about how snowflake gets data teams from any level to elite level performance? Visit adesso SE, October 1st at our booth on the snowflake world tour: Register today: https://lnkd.in/e_qwGZU2

Snowflake just rolled out something game-changing: AI SQL within dynamic tables. 📊 This means you can now enrich your data pipelines with AI-powered insights as the data is ingested, without waiting for post-processing. Imagine: ❄️ Instantly classifying messy data with functions like AI_CLASSIFY ❄️ Running sentiment analysis on customer reviews or survey responses in real time ❄️ Unlocking smarter, higher-value transformations than traditional ETL Instead of bolting on AI later, you can now bake intelligence directly into your pipelines. #AI #MachineLearning #SnowflakePartner #GotCortex?