Systems | Development | Analytics | API | Testing

February introduced key enhancements to Katalon Studio, Runtime Engine, and TestCloud, boosting test automation efficiency, performance, and user experience. These updates reinforce our commitment to continuous improvement, enabling teams to test faster and more reliably. Here’s what’s new this month.

Are you struggling with slow deployment cycles and unpredictable performance issues? Discover how integrating LoadFocus API with Azure DevOps can revolutionize your testing approach and deliver lightning-fast, reliable applications.

The API landscape is constantly evolving, and developers like us crave tools that streamline data fetching and boost performance. For years, REST reigned supreme, but a challenger has emerged – GraphQL. So, is GraphQL truly a "better version" of REST? Let’s explore this question through code and performance comparisons.

The digital landscape is rapidly transforming, with Gartner projecting cloud-native platforms will dominate over 95% of new workloads in 2025. While this shift unlocks agility and innovation, it also introduces significant complexities. Traditional testing methods struggle to keep pace with the dynamic nature of microservices, containers, and distributed systems inherent in cloud-native applications. This is precisely why specialized cloud native testing isn't just beneficial – it's essential.

One of the fastest-growing trends in the datasphere today is the development and use of data products. As we move deeper into 2025 and beyond, data product usage is going to explode. In fact, according to the Gartner 2024 Hype Cycle for Data Management, Data Products are currently at the Peak of Inflated Expectations, signaling strong interest and rapid evolution in this space. You can download the entire report here. The advantages and impact of this new paradigm extend beyond data engineers.

Google Cloud Platform, or GCP, is a cloud resources collection offered by Google for enterprise and standard users. GCP offers a wide range of cloud services, including compute, storage, networking, security, analytics, and even machine learning models. Google Cloud products are the backbone of many cloud applications. Google Cloud allows flexibility with the scalable and predictable cost management.

If you’re building AI systems—whether for personalization, fraud detection, or predictive analytics—you already know the brutal truth: your models are only as good as the data you feed them. Enter Countly, an analytics platform that doesn’t just collect user data but transforms it into rocket fuel for AI. This isn’t about explaining what Countly does (AI can do that for you).

First published on platformengineering.com. Application modernization has become a critical initiative for organizations looking to remain competitive in today’s rapidly evolving digital landscape. As businesses embrace cloud computing, microservices and AI-driven automation, modernizing legacy applications is no longer optional. It’s essential for scalability, agility and innovation.

Quality engineering and assurance efforts, and testing as a part of those, have never been more critical than they are today.

Centralized data allows data teams to dedicate their effort to last-mile analytics and AI challenges.