Systems | Development | Analytics | API | Testing

Human biology is extraordinarily complex, and researchers often have only fragments of information to work with. Brian Martin compares it to looking at a skyscraper through a keyhole: you can see something clearly, but only a tiny piece of the whole. Recorded at EVOLVE26 Singapore, this episode of The AI Forecast brings Paul Muller together with Brian Martin, CTO of Applied AI at Cloudera and co-founder of Rare Hopes NFP, to explore what one of the world’s most data-intensive industries can teach us about AI and decision-making.

Small files are a common problem in Lakehouse tables such as Apache Iceberg, particularly with streaming workloads that continuously commit data in small batches. In this episode, Dipankar explores how Apache Iceberg compaction helps address the small file problem.

Enterprise AI loves a shiny object. Sol Rashidi would rather talk about procurement. After years of leading AI deployments, Sol has learned that some of the biggest wins come from the decidedly unglamorous parts of the business. Her favorite function to transform? Procurement.

80% cheaper AI model. Nearly 2X the data warehouse bill. Switching to a cheaper AI model can cut your token costs by 80%, but it might accidentally double your public cloud data warehouse bill. In this video, we break down the "Agentic Cost Shift"—how autonomous AI agents generate hidden, high-compute SQL queries that drive up data lakehouse costs—and how to fix it using Cloudera. See where the real cost of AI is shifting.

Is your data actually ready for AI? While companies rush to deploy machine learning models, 83% of executives realize that high-value, real-time data is trapped at the physical edge—on factory floors, inside hospitals, and at retail terminals. With billions of connected IoT devices, managing this data creates massive hidden headaches like security risks and pipeline blind spots. True AI readiness starts at the edge. Bridge the gap between your edge devices and your AI goals today.

Paul McDonough-Smith estimates that many business leaders would struggle to define their organization’s problem clearly in fewer than 25 words. With AI, that lack of clarity can quickly turn into fragmented solutions and misplaced expectations. In this episode of The AI Forecast, Paul Muller sits down with Paul McDonough-Smith, a Visiting Senior Lecturer at MIT Sloan School of Management and a Senior Advisor to NASA's Goddard Space Flight Center, to explore how organizations can approach AI with greater clarity and purpose.

As enterprise AI scales, traditional data architectures are reaching their limit. Workloads are becoming more distributed, data movement is accelerating, and tech leaders face growing pressure to justify AI spend while delivering real business impact. Chief Technology Officer Sergio Gago breaks down key findings from Cloudera’s latest global survey of enterprise architects, data architects, and cloud leaders.

Today’s enterprises face immense pressure: scaling fast, staying compliant, and unlocking AI-driven insights—all while fighting siloed data and growing cloud complexity. There is a better way forward, and it starts with Cloudera’s vision for the cloud experience anywhere. Cloudera is the only data and AI platform that delivers the cloud experience anywhere—public clouds, data centers, and the edge—bringing unified security, governance, and control to data wherever it resides. Access 100% of your data for AI-driven insights and future-proof—not just modernize–your enterprise data strategy.

Now that AI coding tools have put development capabilities into more hands, prototypes are becoming business-critical applications almost overnight. Shanea Leven sees an opportunity for a new generation of builders, provided the infrastructure around their applications keeps pace. Shanea explains how organizations can give developers and new technical employees room to build while maintaining the standards required for enterprise software..

Learn how to build an end-to-end, real-time edge-to-AI data pipeline to tackle critical enterprise challenges like credit card fraud detection. In this demo, Diby Malakar (Product Lead for Data in Motion) demonstrates how to process an average of 5,000 transactions per second in low hundreds of milliseconds to detect fraud instantly. Discover how Cloudera’s Data in Motion suite enables application developers to ingest, govern, and enrich streaming edge data to power instant AI model inference and live analytics.