Systems | Development | Analytics | API | Testing

Discover how Cisco is transforming its global data strategy to drive faster decisions, accelerate AI innovation, and scale globally — all while maintaining enterprise-grade security and control.

If you’d like to try Yellowfin for yourself, go ahead and request a free trial. Generative AI has made it easier than ever to analyze data with plain English. You’ve probably seen dozens of videos showing how to use ChatGPT for data analysis — upload a CSV, ask a question, and get a chart. But I wanted to see how that stacks up against Yellowfin, which is designed for analytics from the ground up.

Picture this: You’re a CFO presenting quarterly results to the board. A sharp-eyed member questions a major variance in your forecasting model. You’re ready, you explain that your AI system caught the anomaly early. But then comes the kicker: “How exactly did the AI reach that conclusion?” Suddenly, your confidence wavers. If your best answer is, “I’m not sure, but the algorithm is sophisticated,” you’re not alone, but you’re on thin ice.

Continuous integration and continuous delivery/deployment (CI/CD) in API management refers to the automation of building, testing, publishing, and promoting APIs across environments (development, staging, and production) using CI/CD practices. It brings DevOps principles to the API lifecycle, enabling faster and more reliable API releases.

As modern software delivery processes accelerate, QA teams inevitably feel pressure to maintain quality at scale. SeaLights addresses this challenge by providing deep visibility into test coverage across every stage of the software development lifecycle. It helps teams identify untested code changes, optimize execution, and deliver confidence at release time.

Behind every smooth user experience is a maze of APIs quietly handling requests, responses, and data flows. This makes APIs critical connectors that enable applications to communicate and share data seamlessly. When these vital conduits fail, the consequences can be severe—system outages, data breaches, and frustrated users. API testing is the unsung hero ensuring your digital world runs smoothly and securely.

Artificial intelligence (AI) is widely used today, from voice assistants to Netflix recommendations, but AI models do not always behave as intended. Testing an app before it is released is standard practice, and similarly, AI models should be thoroughly tested. Testing an AI model can verify that the model’s decisions are accurate, fair, and safe.

AI coding tools are everywhere; they have changed the way we used to code. These days, people are doing vibe coding with the help of these tools. From suggesting code snippets to explaining errors in plain English, these assistants are becoming as common as Stack Overflow tabs. There are only two names that keep popping up in everyone’s conversation when it comes to AI coding tools: Cursor and GitHub Copilot. But here’s the real question: Cursor vs.

The test case review process is how we make sure every test case is clear, correct, and ready to run. We treat it as a simple habit that protects quality before execution. When you follow a strong Test Case Review Process, you reduce rework and raise confidence. You also align teams on what matters and how coverage should look. In this guide, we show you how to review test cases effectively with practical steps and examples. We keep it focused, useful, and easy to apply in your next sprint. Let's dive in!

The longer your test cycles run, the slower your product ships. This is one of the core challenges of modern DevOps. Your CI/CD pipeline is fast until it hits the testing stage. That's where parallel testing comes in. Instead of running tests one by one, you can run them side by side. On multiple environments. Across devices. All at once. The result? Faster builds, quicker feedback, and higher deployment confidence.