Systems | Development | Analytics | API | Testing

We're thrilled to announce the tech preview of Active Tracing, a significant advancement in Kong Konnect. This innovative feature unlocks unparalleled visibility into API traffic as it flows through Kong Gateway, empowering you to optimize performance, troubleshoot issues, and refine your API strategies.

This is the first in a series of posts that are going to follow the fictional performance tester Otto Perf. Otto manages all aspects of performance testing for a fictional company OctoCar that specialise in selling used cars. OctoCar are building a new system to manage the selling of their fleet of cars, and we are going to follow Otto on his journey as he ensures that the new technology platform performs in line with agreed non-functional requirements under peak volumes of load and concurrency.

Data visualization helps people comprehend and attain insight into big data. It represents complex data in visually interesting ways that assist in our understanding, and paves the way for a greater link between the provided raw data, and our overall engagement with it. Nowadays, we accumulate data in ever-increasing sizes. We need an intelligent way to understand vast volumes of information. In analytics, we often use different types of data visualization tools to convey complex data.

Software architecture diagrams are essential for communicating complexity. A well-structured diagram helps engineering teams visualize system design, align on technical decisions, and plan future development while proactively identifying potential issues. With so many architecture diagramming tools available, choosing the right one depends on why you need the diagram in the first place.

The demand for automation testing is skyrocketing as businesses strive for faster software releases and higher quality standards. By 2028, the global automation testing market is expected to surpass $55.2 billion, reflecting the increasing reliance on AI-driven, cloud-based, and DevOps-integrated testing solutions. Selecting the right automation testing company is crucial for ensuring seamless digital transformation, reducing costs, and improving test coverage.

Many financial services companies are experimenting with AI through pilot programs, but several challenges remain for adoption. Key concerns include data security, the accuracy of large language models (LLMs) and the rigorous scrutiny from regulators regarding AI’s role in financial decision-making. Current use cases are largely internal, with some customer-facing chatbot solutions addressing noncritical service inquiries.