Systems | Development | Analytics | API | Testing

Qlik’s partnership with Amazon Web Services (AWS) is at the very core of our cloud data integration and analytics platform. Think of it like the iconic double helix of DNA. Just as DNA’s intertwined strands carry the fundamental instructions for life, the interconnection between Qlik and AWS carries the innovation and reliability that power the future of enterprise analytics, data integration, and AI.

So we have all been hearing a lot about AI lately. Everyone is talking about ChatGPT, OpenAI, Claude, image generation, and now there is a new trend about “Agentic AI.“ I know it is getting confusing with all these fancy terms flying around us. Let me break it down for you in simple terms. You can think of this as your friendly guide to understanding the two biggest players in the AI game right now!

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.