Systems | Development | Analytics | API | Testing

As your application grows, the volume of telemetry data expands with it. Every additional service, customer, and feature generates more log entries. It becomes harder to quickly isolate the events that actually matter. Without the right tools, finding a single root cause can feel like searching for a needle in an ever-growing haystack. You may have already been using log search tools to find and act on errors. However, those tools rarely connect that data back to your API traffic patterns or metrics.

How flexibility and interoperability can help you overcome vendor lock-in.

As AI models become commoditized, the conversation is shifting from building smarter models to building data infrastructure that turns models into real business value. Enterprises are accelerating their adoption of agentic AI—systems that don’t just predict but plan, decide, and act autonomously—across their software and operations.

Streaming at scale just got a lot more powerful and cost-effective. Our Q3 Confluent Cloud launch is packed with innovations to help you do more with less: reduce cloud networking costs while maintaining your security posture, scale effortlessly with boosted connection limits, and build production-ready agentic artificial intelligence (AI) applications with seamless tool integrations.

More and more businesses are now starting to realize the importance of having a mobile app developed to boost their business and increase customer engagement. There is a good reason to develop a mobile app and hire a mobile app developer to do this. Why? 2017 was a historic year because for the first time mobile internet use overtook desktop searches. Many studies also show that more and more people are using mobile apps to access the internet rather than their web browser.

Automation testing has become a core part of modern software development. Teams now rely on their automated tests to speed up releases and reduce human error. But running automated tests is not enough. You need to track the right indicators to see if your efforts are actually paying off. It's also easier to demonstrate QA's impact to leadership if you track the right KPIs.

AI is no longer a fringe experiment: it’s a mainstream mandate. But with that shift comes a new kind of pressure: to act quickly, to appear modern, to bolt on something “intelligent” before someone else does. For many teams, this leads to reactive choices. Features get prioritized because they sound impressive, not because they solve a real user problem. Familiar interfaces get copied instead of questioned.

During the holiday shopping period, one of the nation's leading e-commerce brands finally launched its online AI chatbot solution. Within minutes of launching, enthusiastic customers were asking questions; however, the bot was already struggling to answer simple questions, such as “Where is my order?” and “Do you ship internationally?” The confused users abandoned their carts.