Systems | Development | Analytics | API | Testing

Unified Data Governance for Safe & Trusted AI Agents

Hey, did you know your AI agents could be making decisions based on data they were never meant to see? When enterprise data governance is fragmented across separate tools, it creates severe blind spots. Rogue AI agents can over-index, modify, or even accidentally delete production databases simply because proper data guardrails weren't uniformly enforced. In this video, we tackle the root cause of why 79% of enterprise AI initiatives stall and show you how to build a unified data fabric that secures your hybrid estate.

Real-Time AI: How to Move & Process Data Anywhere with Cloudera

Unlock the full potential of your data fabric and accelerate your AI journey with Cloudera Data in Motion. Many organizations struggle with massive amounts of diverse data spread across different formats, vendors, and locations—whether in the cloud or on-premises data centers. Cloudera provides the scalable, performant data services needed to move and process this information in real-time.

A Deep Dive into Lakehouse Catalogs

What exactly is a Catalog, and why has it become such a critical component of the modern Lakehouse architecture and AI workloads? In this episode, we break down the differences between technical catalogs (metastores) and business catalogs, explore how catalogs enable governance and interoperability, and explain why the Iceberg REST Catalog specification became the open standard for sharing Iceberg tables across platforms without vendor lock-in.

AgentTAM: From Firefighting to Flight Control with Agentic AI

Ready to scale your corporate support from chaotic firefighting to structured flight control? In this comprehensive overview, we explore how Cloudera leverages its own technology stack to develop Agent TAM—a powerful suite of autonomous AI agents designed to unlock institutional knowledge, streamline customer workflows, and eliminate technical debt. Whether you want to build an automated Case Analyzer or an intelligent planning companion, this guide provides the exact architectural blueprint to transition your engineering teams from reactive firefighting to proactive, data-driven automation.

Would You Ask a Chef To Cut With a Bending Blade? | Longview Tax

Chef Laurent trained under Ducasse. Her knife work was flawless. Then the blade bent before it touched the meat. She was told the proper knives were being used elsewhere. These had worked "well enough" before. Well enough is a difficult standard to cook by. It's a worse one for filing taxes. Your tax team knows the feeling. Fragmented data. Manual reconciliation. Every close cycle a race against "well enough.".

Where AI Capital Is Flowing: Insights From The Snowflake-Crunchbase Report

At Snowflake Summit 2026, Gene Teare from Crunchbase and Harsha Kapre, Director at Snowflake Ventures, discuss their joint report examining AI investment trends and capital formation in the data ecosystem. Learn how the market has evolved from 2021's broad exuberance to concentrated investment in AI-ready infrastructure, governance, and agentic applications. Discover what signals indicate a startup is truly enterprise-ready versus experimental, and explore the next phase of capital formation in the agentic AI economy.

Welcome to the Era of Action: A Message from Ketan Karkhanis

ThoughtSpot has been named a Leader in the 2026 Gartner Magic Quadrant for Analytics and Business Intelligence Platforms. We’re celebrating this milestone while building for tomorrow, as we shift from the Era of Reporting into the Era of Action. Hear a message from our CEO Ketan Karkhanis on what this shift means for our customers and partners, and how companies can win in this next era of AI BI.

How AI Inference Is Reshaping Enterprise Infrastructure

Data center teams are skilled at solving familiar problems such as storage outages, missed forecasts, and late refresh cycles. These are known quantities. Teams have playbooks for them. But 2026 has brought a different kind of pressure. After years of enterprise AI investment concentrated almost entirely on model training, the industry has crossed a threshold: the workload that now defines AI infrastructure isn’t building models. It’s running them. Continuously. At scale. Every day.