Systems | Development | Analytics | API | Testing

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.