Systems | Development | Analytics | API | Testing

At Koyeb, we run a serverless platform for deploying production-grade applications on high-performance infrastructure—GPUs, CPUs, and accelerators. You push code or containers; we handle everything from build to global deployment, running workloads in secure, lightweight virtual machines on bare-metal servers around the world.

The Wisdom of Crowds Business Intelligence Market Study from Dresner Advisory Services is one of the most recognized annual benchmarks for BI and analytics. It captures how organizations are investing, what priorities they set, and which technologies are delivering the most value. The 2025 edition highlights steady growth in BI spending and shifts in user objectives, from efficiency and revenue impact to security and data quality.

Picture this: As you’re analyzing sales data in Qlik or in Power BI, you spot an obvious error in a customer name and think “I wish I could just fix this right here instead of sending another email to IT.” If this sounds familiar, you’re not alone. The good news is that the business intelligence (BI) world is finally listening. Gone are the days when BI platforms were solely for consuming and analyzing historical data.

A new MIT study reveals 95% of gen AI pilots fail. But that’s not an AI problem. It’s an implementation problem. The real issue is the messy, fragmented way AI is used. Too many organizations treat AI as a helper on the sidelines—chatbots, copilots, and assistants that wait to be called upon. While helpful, this approach barely scratches the surface of what’s possible. Real transformation happens when AI is embedded directly into the core operations of your enterprise.

Modern innovation demands fast, secure, and flexible access to data. But when organizations deal with scattered databases and strict security policies, manual API development slows everything down. The solution? Automate how APIs are built, secured, and managed—using AI and open-source tools like DreamFactory.

Data activation maximizes the value of existing data infrastructure by reducing manual work, increasing reach, and enabling data teams to drive more strategic impact.

Oracle Cloud’s relentless quarterly update cycle waits for no one. Every three months, new functionality, UI changes, and bug fixes are rolled out – and every organization must test before going live. In regulated industries like pharmaceuticals, life sciences, and manufacturing, these aren’t optional updates; they are compliance-critical moments.

In international QA teams, working across timezones is usually the norm. QA leaders of such teams are expected to coordinate across time zones, tools, and cultures. That makes knowing how to manage a remote QA team a key skill in today’s software development world. When done right, distributed QA teams can deliver faster feedback, better coverage, and 24-hour testing cycles. But without structure, remote testing turns into confusion, missed bugs, and blockers at every stage.

Insurance companies operate in a world where every release carries weight. Policy management system testing must be precise. Legacy system modernization QA must avoid disruptions. And risk-based testing for insurers must cover the most critical business flows without slowing delivery. In this article, you will learn: Let’s get started.