Passerelle’s Post

As Head of Partner and Customer Experience at Passerelle, Premier Services Partner recognition matters to me because it speaks to the trust we’ve built on both sides. With Snowflake, and with the customers we’re working alongside every day. In financial services, we're helping banks and credit unions move beyond fragmented data environments into something they can actually use to run and grow their business. That shows up in the platforms we’re building, in solutions like Data Rocket Banking Assistant, and in the way data is getting closer to the people making decisions. It shows up in our growth and in the customers who are willing to speak on our behalf - data leaders like Joshua Nash, Traci Michel, PMP, John O'Connor, Troy Carmichael and Jared Silver. That’s the part that carries the most weight. The work always comes down to vision and execution. Knowing where to go, and doing the work to get there. That's all Pat Dionne, and the incredible team he's assembled at Passerelle. Proud of what we have built, and appreciate the people at Snowflake who have been our champions, especially James McGeehan, Sean Varney ❄️, Paul Ji and Rob Silva.

Choosing between a data lake and a data warehouse isn’t a storage decision. It’s a strategy decision. We work with teams that need to align data platforms with real business goals. Not just store more data, but enable better decisions, faster analytics, and scalable growth. The organisations that get this right treat architecture as a business lever, not just a technical choice. Read the full blog here: https://lnkd.in/gBHfTDe7 #DataStrategy #DataPlatform #DataArchitecture #Analytics #CloudData #Easycoder #AWS

𝗪𝗵𝘆 𝗗𝗼𝗲𝘀 𝗦𝗻𝗼𝘄𝗳𝗹𝗮𝗸𝗲 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗲 𝘁𝗼 𝗘𝘅𝗰𝗲𝗹 𝗶𝗻 𝘁𝗵𝗲 𝗠𝗼𝗱𝗲𝗿𝗻 𝗗𝗮𝘁𝗮 𝗦𝘁𝗮𝗰𝗸 𝗧𝗼𝗱𝗮𝘆?   It’s simple: architecture + ecosystem. By separating storage and compute, enabling multi-cloud flexibility, and embedding data sharing, Snowflake removes complexity.   𝘉𝘶𝘵, 𝘸𝘩𝘢𝘵'𝘴 𝘵𝘩𝘦 𝘳𝘦𝘢𝘭 𝘴𝘩𝘪𝘧𝘵? It’s evolving from a data warehouse into a full data ecosystem that powers apps, AI, and real-time insights.   Earlier, Snowflake was just a data warehouse tool, but it's becoming a data ecosystem. From “data storage” → to “where business happens.” 𝗥𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗶𝗺𝗽𝗮𝗰𝘁:   Businesses are cutting down ETL complexity, rising insights, and enabling cross-functional teams to work from a single source of truth. #DataAnalytics #Snowflake #CloudComputing #DataEngineering #BigData #AI

❄️ Snowflake: Data, Scaled. In today’s data-driven world, businesses are generating massive volumes of data every second—from customer interactions to real-time application insights. The challenge is no longer just storing data, but scaling it efficiently while maintaining performance, security, and cost control. This is where Snowflake changes the game. Built as a cloud-native data platform, Snowflake enables organizations to scale compute and storage independently, ensuring seamless performance even as data grows exponentially. No bottlenecks. No complex infrastructure. Just fast, flexible, and reliable data operations. 🔹 What makes it powerful? • Elastic scalability that grows with your business • Real-time and high-performance analytics • Support for structured & semi-structured data • Secure data sharing across teams and ecosystems 🔹 The impact? • Faster, data-driven decisions • Reduced operational complexity • Optimized costs with pay-as-you-use • Improved collaboration across teams With Snowflake, organizations can turn data into a true competitive advantage—unlocking insights, driving innovation, and scaling without limits. 🚀 Data isn’t just growing. It’s scaling. And Snowflake is leading the way. At Adiantara, we help enterprises make smarter cloud decisions — driving agility, performance, and long-term value through the right partnerships. 👉 Read this to learn more:https://lnkd.in/gtdJWtme #Snowflake #DataScaled #DataAnalytics #CloudData #DigitalTransformation #BigData #DataDriven #Adiantara

We’re proud to announce our new partnership with Databricks,bringing together powerful capabilities in Data Management, Data Engineering, Data Warehousing, and Data Science. This collaboration enables us to help businesses unlock the full potential of their data,faster, smarter, and at scale. From building robust data pipelines to driving actionable insights, we’re ready to power the next wave of data-driven innovation. #DataIntegration #iPaaS #APImanagement #Orchestration #DigitalTransformation #CloudIntegration #BusinessGrowth #Automation #Melonleaf

Had a great morning at Snowflake's 'Data for Breakfast' - strong speakers and a topic that cuts to the heart of where enterprises are right now: how do you make AI actually work, not just work in demos? Snowflake's framing was solid: AI success requires AI in every workflow, business logic and context embedded in the platform, and a unified data foundation underneath it all. Hard to argue with that. What struck me most was a conversation afterward. Several attendees are already planning to build full DataOps platforms with agentic capabilities. Bold ambition. But the questions I'd want them pressure-testing first: → How long does it realistically take to build and maintain? → What's the TCO when you factor in reliability, governance, and iteration? → What happens when the business requirements change in month 8? After a long data journey, here's what genuinely excites me right now: for the first time, I think we can actually have it all. Enterprise-grade. Unified. And fast enough to match the pace of the business. That trifecta has always been the trade-off nobody could solve - until metadata-driven development changed the rules. With ADE and ADA, you're not rebuilding the foundation every time. The metadata layer gives data teams a governed, contextual understanding of the data estate from day one - delivering that elusive single version of truth alongside the agility the business has always asked for. The goal isn't to avoid building. It's to build smart - and get to business value before the window closes. This is exactly what our team is focused on. If it's a challenge you're navigating too, I'd love to talk - or if you'd rather start with some reading, swing by https://lnkd.in/dG27gJMg. Good event. The best ones always leave you with more questions than you arrived with. ☕ #DataStrategy #EnterpriseAI #Snowflake #Metadata #DataOps ❄️ Markus Salo Olli Ek Janne Viitala

This is some incredible news. Semantic Layers not only change the governance conversation, but add tremendous ROI very quickly.

Invest in a shiny new platform? Or a proven data solution? How do you even decide? DAS42 Principal Consultant Jeff Springer's test: Does it create meaningful, measurable impact on human productivity? "It has to increase productivity in a meaningful and measurable way. It has to impact people's lives." Don't get distracted by bells and whistles. Stick with real value. Snowflake Snowflake Partner Network #TechAdoption #Snowflake #EnterpriseAI

𝗧𝗵𝗲 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗮𝗹 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀 𝗺𝗮𝗱𝗲 𝗮 𝗱𝗲𝗰𝗮𝗱𝗲 𝗮𝗴𝗼 𝗮𝗿𝗲 𝗱𝗶𝗿𝗲𝗰𝘁𝗹𝘆 𝘀𝗵𝗮𝗽𝗶𝗻𝗴 𝘄𝗵𝗮𝘁 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲𝘀 𝗰𝗮𝗻 𝗮𝗰𝗵𝗶𝗲𝘃𝗲 𝘄𝗶𝘁𝗵 𝗔𝗜 𝘁𝗼𝗱𝗮𝘆. Snowflake was not built to chase trends. It was built from first principles — separating compute from storage, treating data as a shared asset, and designing for scale before scale was the obvious problem to solve. Those foundational ideas look remarkably prescient right now. As enterprises race to operationalize AI, the platforms that win will be the ones built with clean data architecture underneath, not bolted-on intelligence on top of fragmented systems. This piece from Snowflake revisits the original thinking behind the platform and connects it to where enterprise data and AI are heading next. For anyone advising organizations on their data strategy, it is worth reading carefully. The principles that made Snowflake work for analytics are the same principles that will determine which companies are ready for the AI era — and which ones are still untangling their data foundations. Read the full article here: https://lnkd.in/gM2NrUFS #Snowflake #EnterpriseAI #DataPlatform #DataStrategy #CloudArchitecture