Berkay Koçak’s Post

Great morning at Snowflake's Data for Breakfast in Buenos Aires! ☀️ It was one of those events where you leave with your head full of ideas — and a coffee in hand. The focus was on Snowflake Intelligence, Cortex, and real business use cases. What stood out most: the shift from "AI as a feature" to AI as a core layer in data architecture. The ability to query your data in plain English, get instant insights, and build AI workflows — all without moving data out of your secure environment — is genuinely impressive. The business cases shared made it tangible: less time on data prep, faster decisions, and teams that can actually self-serve on analytics. Looking forward to exploring what this means for the work we do. 🚀 #Snowflake #DataForBreakfast #BuenosAires #SnowflakeIntelligence #Cortex #DataAnalytics #AI

Last week, I had the opportunity to share our journey of building an internal enterprise intelligence solution at Snowflake's Data for Breakfast at Edinburgh. One theme stood out, both in our experience and across the event: AI is only as good as the foundation it’s built on. It’s tempting to jump straight into models, copilots, and shiny use cases. But without the right architecture, clean data models, and well-governed systems underneath, it’s just a matter of time before things break! Treat the AI is the final layer and not the starting point. :)

Started my Thursday morning with Snowflake ’s Data for Breakfast in Toronto — coffee, keynotes, and a room full of people who geek out over data as much as I do. Solid way to kick off the day. ☕❄️ What struck me most wasn’t the product demos (though those were sharp) — it was the customer stories. Hearing how organizations are actually navigating the shift to agentic intelligence, not just theorizing about it, was genuinely useful. My biggest takeaways: → The orgs winning with AI aren’t the ones with the fanciest models — they’re the ones who sorted out their data foundation first → Agentic AI without governance is a liability. With it, it’s a differentiator. → Data engineering and pipelines are back in the spotlight — the not so pretty work is suddenly very strategic Came back to my desk with new ideas and a few follow-up conversations to have. That’s what a good event does. Big thanks to the Snowflake team 🙌 #Snowflake #DataForBreakfast #Toronto #AgenticAI #DataStrategy #Analytics

The pace of change happening in data engineering seems to be accelerating. I'm grappling with this both as a product marketer trying to understand how the tools Snowflake are building are replacing routine tasks and then the swift adoption of AI to accelerate coding tasks. In this blog post, you'll see how I'm starting to see: ✅ Cortex Code: Building production-grade pipelines with simple prompts. ✅ Dynamic Tables: How companies like Travelpass are seeing huge boosts in productivity. ✅ dbt projects on Snowflake: Running dbt natively for a unified dev experience. Check out the post and let me know your thoughts: https://lnkd.in/gnMvEC3M #DataPlatform #ModernDataStack #dbt #DataQuality #SnowflakeDB

“The first thing our customers notice is how fast their data becomes visible and how much simpler their architecture diagrams become.” That’s exactly what CrateDB is built for. 📘 In this guide, you’ll learn what makes your new architecture fast, scalable, and easy to work with. 👉 A great starting point if you’re curious about modern data systems: https://hubs.ly/Q049-X9s0 #DataEngineering #RealTimeData #Analytics #BigData #AI

Data teams are under more pressure than ever to ship faster, without cutting corners on quality or governance 📦. The problem? Too much time is still going to infrastructure work, brittle pipelines, and firefighting, instead of building the trusted data products the business actually needs. On April 2, Coalesce is joining Snowflake for a live 60-minute session on how #AI is changing the day-to-day reality of data engineering work. You'll see: 🔹 How AI can accelerate transformation logic, data modeling techniques, and pipeline development without removing human judgment from the equation 🔹 How to increase delivery velocity while keeping governance, lineage, and testing intact 🔹 Live demos from both Snowflake and Coalesce showing what this looks like in practice Join us for a practical look at how modern data pipeline development is evolving, with real examples you can take back to your team: https://lnkd.in/gbk7MVpS

Coalesce is teaming up with Snowflake for a session on how AI is reshaping day-to-day data engineering, helping teams move faster without compromising on quality or governance. Hope to see some of you there 👇

Most #AI problems are actually data problems. If your data pipelines are messy, your AI will be too. Join Coalesce.io and Snowflake teams on 2nd of April to learn how you can accelerate you data pipeline development whilst keeping focus on: 👉 built-in governance & observability 👉 real-time lineage 👉 AI that actually understands your data context

I started wondering, how far can AI go in the data world? Could it ever replace data engineers, analysts, or architects? In just one week, I built a very junior data bot. It can run basic validations from documentation and even extract data lineage from Snowflake. The takeaway? AI won’t replace senior-level expertise anytime soon, but it can speed up their work by 5x or more. Curious about what’s already out there, I explored the market and found something impressive: Upriver automates enterprise data engineering with AI-driven pipelines. The future of data is already here.