How to reimagine data architecture for AI success

For AI to succeed, organizations must fundamentally reimagine data architectures. Learn more about architecture requirements for building agentic AI via Mohan Varthakavi #Couchbase

🏗️ 95% of enterprise AI pilots are failing—not because of the AI models, but because of poor data foundations. The Medallion architecture (Bronze → Silver → Gold) offers a framework for progressive data refinement. Combined with Incorta's Direct Data Mapping™ and real-time capabilities, you can transform fragmented data into AI-ready insights that actually deliver business impact. Your data architecture isn't just about storage—it's about creating the foundation for AI success. Read more at https://lnkd.in/gdh6FXrg #DataArchitecture #AI #DataStrategy

Are poor data foundations hurting your companies ability to take advantage of AI? If so, read this insightful article by my friend and colleague Amit Kothari on how to leverage Incorta to build your data foundation. #data, #GenAI, #datafoundation

Before diving into Balaji Venugopal’s article, I just want to say—it’s a must-read for anyone serious about building a future-proof data architecture for AI. Clear, practical, and refreshingly honest. Worth your time.

Understanding Data Ingestion Architecture: Why It’s the Foundation for AI Success As organizations scale from terabytes to petabytes of data, the biggest hidden challenge in AI projects is not simply accessing data, it’s ingesting it in a way that makes it usable. Data ingestion is often underestimated, yet it’s the foundation that determines whether data can truly power analytics and AI. Without strong ingestion pipelines, projects stall under the weight of silos, slow processing, and complex governance requirements. Evan Smith breaks down the fundamentals of batch vs. streaming ingestion, explains why ingestion is mission-critical for AI, and shows how Starburst helps teams simplify with Managed Iceberg Pipelines. The result: less time building and maintaining pipelines, more time delivering value from your data. Read the full post here: https://okt.to/tAYXS4 #DataIngestion #DataArchitecture #ArtificialIntelligence #StarburstData #ApacheIceberg

New Article: Open Data Fabric – Rethinking Data Architecture for AI at Scale Enterprises are racing to put AI agents into production. But too many are finding that what works in a demo fails in the real world. The issue isn’t the agents – it’s the data architecture they’re forced to run on. Today’s “modern data stack” was built for humans and dashboards. AI agents need something different: ✅ Real-time access to all enterprise data (not batch refreshes) ✅ Rich business context to prevent hallucinations ✅ A collaborative, iterative workflow that supports self-service at machine speed This is where the Open Data Fabric comes in. Instead of forcing everything into a single vendor’s stack, it provides: - Unified data access across distributed systems without duplication - Contextual intelligence that grounds AI in business meaning - Collaborative self-service where humans and agents refine, share, and trust results Read the full breakdown from CEO Prat Moghe on why the right data foundation is the key to making enterprise AI actually work 👇 👉 https://lnkd.in/eCZNeBMM

The proliferation of data sources and the demand for real-time AI insights are driving a re-evaluation of traditional data architectures.

Over the years, #DataEngineering has been about building big pipelines and moving data at scale. We’ve all been there—spending hours fixing broken jobs, chasing schema changes, or answering the never-ending question: “Where did this number even come from?” At #UnlockTheNxt, we’ve realized the real challenge isn’t just moving data anymore. It’s understanding it. That’s why we believe #metadata is becoming the real game-changer. When pipelines are metadata-driven, they don’t just move rows and columns. They explain the story behind the data—who owns it, how it changed, and why it can be trusted. The shift is powerful: Governance becomes built-in, not bolted on. Pipelines adapt when things change, instead of breaking. Business leaders gain trust because every number comes with its own lineage. #AI systems get data that’s transparent, explainable, and reliable. To us, metadata isn’t just “data about data.” It’s the foundation for the next era of data engineering. We wrote an article about this shift, and why it matters more than ever. If you’re curious about the future of data, we’d love for you to give it a read. 👉 https://lnkd.in/gSnVc9F2 "In the era of intelligent systems, data without metadata is like a map without directions — you may know the terrain, but you’ll never reach the destination." #DataEngineering #Metadata #UnlockTheNxt

🚀 The Great Pipeline Convergence: When AI Meets Real-Time Data Integration The data engineering landscape just hit a major inflection point. Airbyte's enhanced ClickHouse connector now delivers 10GB/s throughput with millisecond precision, while Snowflake Openflow revolutionizes multimodal data movement through Apache NiFi's enterprise-grade foundation. But here's what's truly game-changing: these aren't isolated improvements—they're building blocks for AI-native data architectures. The Numbers That Matter: ✨ 10-20x ROI on federal IoT infrastructure investments driving massive data generation ⚡ 5-second query latency with Snowflake's streaming integration capabilities 🔄 Real-time bidirectional flows enabling AI agents to make decisions at machine speed Why This Convergence Is Unprecedented: Traditional data pipelines were built for humans making decisions. Modern pipelines serve AI agents that process thousands of decisions per second. Airbyte's latest ClickHouse improvements support all sync modes with direct schema mapping—perfect for feeding vector databases that power RAG workflows. Meanwhile, Snowflake Openflow transforms the integration game by supporting unstructured data preprocessing with native Cortex AI capabilities. This isn't just data movement; it's intelligent data preparation happening at ingestion time. The Federal Catalyst: NIST's IoT infrastructure study revealing 10-20x returns signals massive data volume expansion. Federal agencies are deploying data-driven IoT solutions across transportation, grid modernization, and critical infrastructure. This creates unprecedented demand for pipelines that can handle structured sensor data, unstructured documents, and real-time streams simultaneously. What's Actually Revolutionary: The convergence enables AI-first data architecture where: Pipelines automatically adapt to schema changes without human intervention Real-time transformation happens inline with ingestion AI agents can trigger data flows based on business logic Multi-cloud deployments maintain consistent governance AI-powered data orchestration with intelligent pipeline management The Strategic Shift: We're moving from "build pipelines for analytics" to "architect data flows for autonomous systems." Organizations implementing this convergence report dramatic improvements in AI model performance because data reaches models faster, cleaner, and more contextually rich. This isn't just about better tools—it's about enabling a future where data engineering infrastructure anticipates and serves AI needs automatically. The question isn't whether to adopt these technologies; it's how quickly you can orchestrate them into a unified, AI-ready platform. Ready to architect the future of data engineering? #DataEngineering #AI #Airbyte #ClickHouse #SnowflakeOpenflow #RealTimeData #DataArchitecture #MachineLearning

🗓️ Excited for the virtual Data & AI Architecture Summit on September 23! Can’t wait to hear from inspiring speakers at Informatica and Deloitte as they share the latest on data architecture and agentic AI. Save your spot. #AgenticAI #AI