CrateDB’s Post

3 barriers preventing organizations from scaling data ⚙️ In our project experience, we repeatedly observe three common challenges: 1️⃣ Data is fragmented across systems and regions. 2️⃣ Architecture is not designed for AI and ML. 3️⃣ Data access takes longer than decision-making cycles. During Data for Breakfast, we will demonstrate how to build an environment where: 🔸 data is available in a self-service model; 🔸 governance supports - not blocks - innovation; 🔸 infrastructure enables scalability instead of limiting it. As a Black Diamond Partner, we will share real enterprise implementation scenarios and lessons learned. 💡 This will not be a discussion about trends - but about executing a real data strategy. 📍 Warsaw 🔗 Registration: https://lnkd.in/gZWaPeTm If data is meant to support your business - not become its cost burden - this event is worth attending. #DataArchitecture #BPXExperts #ModernDataPlatform #EnterpriseData #AnalyticsStrategy #AITransformation #CloudInfrastructure

Financial services organizations are under pressure to make faster, more accurate decisions—but fragmented, siloed data is holding many of them back. It’s time for a unified, governed data foundation that enables real-time insights and AI at scale. ⬇️ Join Josh Kohr, Heather Rhyne-Christensen, and Keith Evans for a live session focused on how leading financial institutions are modernizing their data architecture to unify data across lines of business, strengthen governance, and unlock AI-driven decision-making. You'll want these takeaways: ✅ Why fragmented data architectures limit decision-making and client experience ✅ How Snowflake supports secure data sharing, governance, and AI at scale ✅ How to break down silos across customer, risk, and financial data ✅ A practical framework for designing your architecture Save your spot now (+ get the recording later): https://lnkd.in/eYbDZwni

I've talked to a number of enterprise data leaders over the past year. The frustration is always one of four things. 📝 Which one is yours? → Data pipelines are fragile and constantly breaking → Real-time data is still a goal, not a reality → AI initiatives are blocked by poor data access → Business teams don't trust the numbers they're getting Vote or comment below with more context. The answer isn't always more tools. Sometimes it's the right architecture. #RealTimeData #DataProducts #SAPData #EnterpriseAI #DigitalTransformation

Is your Data Lake becoming a Data Swamp? Storing raw data is no longer enough. In a world that demands instant answers, enterprises are moving toward a Data Fabric—a dynamic, AI-driven architecture that connects siloed information for real-time decision-making. Discover how Ceyoon leverages AI-Augmented Engineering to architect resilient data ecosystems that transform complex information into immediate business value. Read our latest technical insight: https://lnkd.in/gnJ_p47B #Ceyoon #DataFabric #BigData #RealTimeAnalytics #EnterpriseArchitecture #AIEngineering #TechTrends2026 #DataStrategy

Qlik Expands Agentic Data Engineering to Accelerate AI-Ready Data Delivery “Most companies do not struggle to imagine AI use cases. They struggle to deliver the trusted, current data those use cases depend on, As demand rises, data engineering becomes the critical path. Qlik is helping teams reduce friction, protect trust, and keep pace with the business.” said Mike Capone, CEO, Qlik. Read More: https://lnkd.in/dP-9FV_J #agenticanalytics #AgenticDataEngineering #agenticexecutionstrategy #AIinfrastructure #AIReadyDataDelivery #datascience #ITDigest #news #Qlik

Most teams aren’t hitting limits with AI models. They’re hitting limits with their data architecture. The latest post from Fivetran breaks down Open Data Infrastructure and why storing data once in open formats, with flexible access across engines, is becoming the foundation for analytics and AI at scale. Worth a read if you’re thinking about long-term architecture: https://lnkd.in/dT6Bnyd6

Most teams aren’t hitting limits with AI models. They’re hitting limits with their data architecture. The latest post from Fivetran breaks down Open Data Infrastructure and why storing data once in open formats, with flexible access across engines, is becoming the foundation for analytics and AI at scale. Worth a read if you’re thinking about long-term architecture: https://lnkd.in/e33PReEh

Most teams aren’t hitting limits with AI models. They’re hitting limits with their data architecture. The latest post from Fivetran breaks down Open Data Infrastructure and why storing data once in open formats, with flexible access across engines, is becoming the foundation for analytics and AI at scale. Worth a read if you’re thinking about long-term architecture: https://lnkd.in/gxDR3tnZ

Most teams aren’t hitting limits with AI models. They’re hitting limits with their data architecture. The latest post from Fivetran breaks down Open Data Infrastructure and why storing data once in open formats, with flexible access across engines, is becoming the foundation for analytics and AI at scale. Worth a read if you’re thinking about long-term architecture: https://lnkd.in/g-RZmJHG