Systems | Development | Analytics | API | Testing

Every QA in global releases tells two stories: the one the teams plan for and the one users experience. And often, those stories don’t match.

Learn what data orchestration is and how it differs from ETL. Explore example data orchestration workflows and top tools to automate your data workflows.

Right now, many SaaS leaders are wondering how AI will change building and scaling software companies? AI is transforming how we build software, how teams operate, and how quickly companies launch new products. According to Adam Robinson, founder and CEO of Retention.com, there’s something that most leaders overlook. Your problems won’t get solved by AI but by product-market fit.

SwiftUI Previews show us how our app will look out in the wild and let us make changes in real time, without emulators. But that’s not the full story. The full benefit of SwiftUI Previews lies in declarativeUI, which allows us to dictate the final state we want to achieve and handles all the process stuff itself. This is a game-changer for developers, allowing us to shift our focus from ‘how’ to ‘what’.

Data masking vs. tokenization — which should your organization be using to protect sensitive data? The simplest answer: if you need to easily re-access original data, tokenization is preferable. If you need irreversibly transformed data for development or analytics, masking is the superior choice. This is especially true when it comes to using data for artificial intelligence (AI).

The European healthcare landscape is undergoing its most significant digital transformation in decades. We are moving away from a fragmented era where health data was locked within the walls of individual hospitals and national borders. In its place, the European Health Data Space (EHDS) is emerging, a unified digital ecosystem designed to give patients control over their data and unleash its potential for research and innovation.

AI can now generate SQL, build dashboards, and answer questions in plain language. But generating queries isn’t the same as understanding a business. The model might not know which revenue definition finance approves, how your fiscal calendar works, or which fields require restricted access. As AI agents become the front door to analytics, the real challenge isn’t query generation; it’s semantic grounding. That’s where the Agentic Semantic Layer becomes essential.

The promise of test automation was simple: automate repetitive testing tasks, catch bugs faster, and ship quality software at scale. Yet for most development teams, that promise remains unfulfilled. Traditional test automation frameworks demand specialized coding skills, require constant maintenance when applications change, and create bottlenecks that slow down release cycles rather than accelerate them.

As enterprises race to deploy multicloud architectures and Agentic AI, they face a common bottleneck: "state." To govern AI token usage, manage agent-to-agent communication, or optimize performance via caching, API and AI gateways require a persistence layer to synchronize data. We’re excited to share the GA of Managed Redis cache for Kong Dedicated Cloud Gateways (DCGW).

A persistent challenge arises as businesses adopt multicloud architectures and agentic AI: the need for state synchronization. API and AI gateways require a robust persistence layer to synchronize data, whether it's for governing AI token usage, facilitating agent-to-agent communication, or boosting performance through caching.