Systems | Development | Analytics | API | Testing

Most organizations are investing heavily in AI—but the outputs still aren’t reliable. The reason often isn’t the model. It’s the data pipeline behind it. Disconnected systems, inconsistent preparation, and limited governance make it difficult for AI to produce accurate answers. Before AI can deliver real value, the data feeding it must be structured, contextualized, and governed. In this animation, we break down the 5 Pillars of AI-Ready Data and show how data moves through a connected pipeline before it reaches AI.

Multi-agent systems aren't new architecture—they're microservices evolved. Varun Jasti of AWS explains why Apache Kafka is the natural backbone for agent-to-agent communication at scale.

From semiconductor fabs to AAA game studios, Perforce P4 is the version control platform behind the world's most ambitious creative and engineering work. Global industry leaders, including NVIDIA, Samsung, DNEG, Nickelodeon, Sandfall Interactive, Warhorse, and BMW use Perforce P4 to collaborate without boundaries, protect critical IP, and build things the rest of the world relies on. P4 is the version control foundation for.

Dive into the pros and cons of data masking and synthetic data generation, with real-world examples and practical use cases. In this webinar, Perforce Delphix experts Ilker Taskaya and Hims Pawar show you how to select the right approach based on your specific needs, compliance requirements, and data accessibility challenges.

In this video, Dipankar breaks down how Apache Iceberg works under the hood - starting from the limitations of Hive-style tables to why Iceberg was built in the first place. What you’ll learn: The shift from directory-based to metadata-driven architecture. How Iceberg tracks files on S3/Object Storage. Why abstraction is the key to scaling your data platform.

Stop your AI projects from being abandoned due to a lack of data readiness. Cloudera AI provides the tools to secure, govern, and prepare your data for production, no matter where it lives. Turbocharge your AI journey today. Contact your Cloudera representative to learn more. *Read More:* Check out our blog post on solving the AI backlog.

In today's geopolitical environment, asking 'are we compliant?' is no longer enough. The real question is whether your business can survive if the ground shifts beneath it — through trade wars, embargoes, or regulatory changes that threaten your software supply chain.

Most AI failures aren’t model problems. They’re data pipeline problems. Disconnected systems. Inconsistent preparation. No governance at query time. This short animation walks through the 5 Pillars of AI-Ready Data and shows how data needs to move through a structured pipeline before it can power reliable AI. 5 Pillars of AI-Ready Data Access → Prep → Context → Governance → Monitoring Five stages. One connected flow.

Is your AI agent one misconfigured server away from a production data leak? In this deep dive, Jeremy from Lenses explores the critical security architecture of the Model Context Protocol (MCP) and how it’s evolving to protect the future of Agentic Engineering.

Discover the powerful new IDE-like Studio in Lenses 6.2. Learn how to manage your Kafka clusters, discover topics across multiple environments, and perform side-by-side comparisons of dev and staging data. We also dive into the new ways to interact with streaming data, including the CLI, VS Code plugin, and the new MCP server for AI agents and chatbots. Whether you're a developer troubleshooting schema mismatches or a data engineer managing complex Kafka estates, the new Lenses Studio provides the tools you need to stay in context and work efficiently.