Open lakehouse architecture is becoming the foundation for production AI and enterprise AI at scale.  In this episode of The AI Forecast, Dipankar Mazumdar, Director of Developer Relations at Cloudera and co-author of the book “Engineering Lakehouse with Open Table Formats,” joins Paul Muller to explain why open lakehouse architecture is critical for moving from AI pilot to production AI.  They break down:  ✅ How Apache Iceberg and open table formats decouple storage from compute ✅ How schema evolution enables change without costly data rewrites ✅ How multiple engines can securely access the same data without duplication ✅ How to prevent small-file performance bottlenecks ✅ How to control AI compute costs at scale ✅ How to embed governance, metadata, and data lineage into AI workloads   Production-ready AI requires scalable data architecture and governance built in from day one. AI and GenAI pilots may be everywhere, but your architecture is what truly decides what survives.   *Stay in touch with Dipankar:*  Dipankar Mazumdar on LinkedIn: https://www.linkedin.com/in/dipankar-mazumdar/  Dipankar’s website: https://dipankarmazumdar.github.io/   Dipankar’s book on Amazon: https://www.amazon.com/Engineering-Lakehouses-Open-Table-Formats-ebook/dp/B0DKJD39X8   *Links & Resources* 👉Full Series Playlist: https://www.youtube.com/playlist?list=PLe-h9HrA9qfAmGHgsmXUZgLL-T4Xjhlq8 👉Learn more: https://www.cloudera.com/resources/podcast/the-ai-forecast.html *Follow and subscribe to The AI Forecast for more conversations with the innovators shaping the future of enterprise AI.* *🎧 Listen on your platform of choice:* Spotify: https://open.spotify.com/episode/2lAy022x8572o5wSNZ2t55 Apple: https://podcasts.apple.com/ie/podcast/open-lakehouse-architecture-how-to-scale-ai-to-production/id1779293119?i=1000753020221 Chapters: 00:00 Intro: What is the future of enterprise AI? 01:44 The Fast Four: Getting to know Dipankar Mazumdar 06:51 What are the 3 best practices for working with data? 09:27 How did Dipankar become a leader in Developer Advocacy? 14:10 What exactly is a Data Lakehouse and why do you need it? 20:39 Why should you use Open Table Formats for AI? 25:35 What are the common misconceptions in Lakehouse adoption? 28:43 How to solve the "Small Files" problem in streaming workloads? 34:26 How to balance cloud costs and context engineering for AI? 39:31 How does Lakehouse architecture drive real business value? 41:52 Why are Governance and Lineage non-negotiable for AI? 44:22 What are the operational pain points in moving from POC to Production? 47:25 How do Lakehouses power the future of Generative AI? 51:09 Conclusion: Where to learn more about Open Table Formats *Connect with Cloudera* Subscribe to stay ahead of the curve with the latest in data strategy, open architectures, and enterprise AI innovations. https://www.cloudera.com LinkedIn ► https://www.linkedin.com/company/cloudera Facebook ► https://www.facebook.com/cloudera/ X ► https://x.com/cloudera Spotify ► https://open.spotify.com/show/102S8zoZR6nmZV0HxZlxZu #DataLakehouse #ApacheIceberg #DataEngineering #Cloudera #OpenTableFormats #DataArchitecture #GenerativeAI #TechPodcast #DataScience #DataOps #IcebergArchitecture #ProductionAI

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