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
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We’re heading to the Data Innovation Summit in Stockholm 🇸🇪 Enterprise data teams are under pressure to support AI, real-time analytics, and constant change but most platforms weren’t built to scale architecture, governance, and delivery together. That’s where data automation changes the equation. At Booth C68, we’ll be showing how teams are: Automating data modeling, ELT, and orchestration from metadata Scaling Data Vault 2.0 and dimensional models without rebuilding logic Embedding lineage, auditability, and governance by default Modernizing legacy SQL and on-prem environments into Azure and Snowflake 🎤 Also catch our session: “Turning Data into Decisive Power – Pohjolan Voima Prepares for AI with a High-Quality Data Platform” 📅 May 7 | 11:00–11:20 AM 🎙️ Antti Kajala, CIO, WiseDigi Oy If you’re dealing with slow development cycles, fragile pipelines, or manual rebuilds during modernization, this is worth your time. See you in Stockholm: https://ow.ly/w5jm50YFIIv #DataInnovationSummit #DataEngineering #DataArchitecture #DataAutomation #DataVault #Snowflake #Azure
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If you are building your first data lakehouse or rethinking an existing architecture, you cannot miss this session at the Data Innovation Summit 2026! Round of applause for Roland Wammers, Field Architect at MinIO and his presentation on the topic of “One Store to Rule Them All: Apache Iceberg V3 and the sovereign Object-Native Lakehouse.” Roland will explore how MinIO AIStor collapses the traditional divide between object storage and analytical data stores by embedding the full Apache Iceberg V3 Catalog REST API natively into the data layer, a first in the industry. He will cover how this eliminates the overhead of managing separate catalog infrastructure, how it integrates out of the box with Spark, Trino, Dremio, Starburst and others, and why it offers a compelling path for enterprises looking to modernize their lakehouse strategy on-premises with full data sovereignty and without hyperscaler lock-in. Book your tickets because this session will give you a clear picture of what truly unified, high-performance analytical storage looks like in practice. 👉 Don’t miss this session - secure your ticket now and join 3,500+ data, analytics, and AI leaders at DIS2026! https://hubs.li/Q047hBHk0 #DISummit2030 #DIS2026x1
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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
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Why graph8’s data architecture matters for the entire platform: The enrichment waterfall doesn’t just return contact info. It returns structured metadata: - Confidence scores per field (email: 98%, phone: 75%, title: 95%) - Source attribution (which providers contributed which fields) - Freshness indicators (when each field was last validated) - Change signals (fields that differ from previous lookups) This metadata powers downstream features: - Campaign engine uses confidence scores to prioritize contacts - Sequence engine routes high-confidence phones to call steps - Analytics track data quality alongside campaign performance The data layer isn’t just “contacts.” It’s an intelligence layer that makes everything else smarter. 10,000 free contact credits + 2,500 AI credits at graph8. #DataEngineering #Architecture #Intelligence
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Your #data isn’t fragmented. Your architecture is. Most enterprises scale data. Few make it intelligent. An AI-blended data fabric turns chaos into clarity, connecting, governing, and activating data in real time to power true business outcomes. The future isn’t managing data. It’s letting data manage itself. Insights by Mataprasad A.. Learn more: https://lnkd.in/d6uUxeDS #DataFabric #DataArchitecture #ArtificialIntelligence #DataStrategy #DigitalTransformation #DataEngineering #AIInnovation #BigData #CloudComputing #EnterpriseData
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The cbs practice “Technology, Data & Architecture” recently retreated to the Sauerland region for an intensive, cross-disciplinary collaboration on key topics in technology, data management, analytics, and AI architecture. A great example of #ThePowerOfOrange in action.
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“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
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Pipelines are failing quietly, companies have metrics that don’t reconcile and dashboards that they do not fully trust. Often the issue isn’t the tool. It’s the data architecture underneath them. Primastat focuses on engineering reliable data foundations — the systems that make analytics and AI actually work.
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Your data architecture is costing you more than you think. Rigid warehouses. Messy lakes. Siloed systems. Three different structures, three different failure modes, one shared outcome: analysts waiting, AI stalled, and costs climbing. The problem is not the data. It is the sprawl. What does your current setup look like? #DataAnalytics #BI #DataStrategy #AIReadiness #AnalystProductivity #DataGovernance #DataEngineering #Consulting
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👉Dremio argues that enterprises embraced Iceberg because they wanted interoperability and less lock-in, and that the format has also become increasingly important for AI-era data architectures that need access to structured, semi-structured and unstructured data in one lakehouse. https://lnkd.in/dPWsrq_h Dremio #ApacheIceberg #Icebergtableformat #datalakehouse #opentableformat #ApachePolaris #IcebergV3 #AIdataarchitecture #analyticsplatform #Databricks #Snowflake
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