Systems | Development | Analytics | API | Testing

Your pipeline is green. But do you actually know what you tested? Most teams don’t know what changed, what was covered, or what risk remains. That’s the gap Quality Intelligence solves. It turns test and engineering data into real, evidence-based confidence so you can release faster, with less risk. With Tricentis SeaLights, you can move from assumption to understanding. So you don’t just test more, you understand more!

"Every great invention takes time to reach its full potential. Just as the light bulb required a system to illuminate the world, artificial intelligence requires the foundation of process to reliably deliver value.".

Check out what’s new in ThoughtSpot’s latest release. dbt MetricFlow Integration: Seamlessly import semantic definitions from dbt for a single source of truth across your stack. AI Theme Builder: Stop mapping CSS. Describe your brand guidelines and watch a polished UI appear instantly. Enhanced Mobile Experience: Bring decision-making to your pocket with expert-level reasoning via Spotter 3 and mobile-first Muze charts.

Data trust is broken. In the "good old days," one expert vetted one dashboard. Today? You have massive scale and AI models that need accurate data to survive. Jessica Talisman joins Cindi Howson on The Data & AI Chief to reveal why the ontology pipeline is the secret sauce for trustworthy AI. Learn how structural clarity turns data chaos into your biggest competitive advantage. Catch the full discussion on your preferred podcast player!

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.