Systems | Development | Analytics | API | Testing

API traffic now accounts for 57% of all Internet activity, fueled by AI adoption and the growing demand for real-time data. But this surge comes with challenges: complex development, lack of standardization, and security risks. Automated API generation is the solution.

When you’ve worked with data at scale, you come to appreciate the beauty and the complexity of systems like Kafka. With nearly 30 years of experience navigating the evolution of technology and data platforms, I’ve seen firsthand how Kafka has revolutionized real-time data processing - and where it can challenge even the best teams. The real question is: How do you move past those challenges to unleash everything Kafka has to offer?

From automating routine tasks to providing real-time insights to inform complex decisions, AI agents and copilots are poised to become an integral part of enterprise operations. At least that’s true for the organizations that can figure out how to supply large language models (LLMs) with real-time, contextualized, and trustworthy data in a secure and scalable way.

Low-code testing tools simplify the testing process. All of the complexity of coding is taken care of by the features designed by the development team of the tool. Thanks to these low-code tools, a little bit of technical know-how is more than enough to start testing. It opens up QA to a broader audience. In this article, we review the top low-code testing tools in 2025.

Kubernetes (or K8s) is inherently complex, making it challenging to grasp and even harder to implement in deployments—especially for developers new to the technology.In addition to that, managing code changes in a Kubernetes cluster can be complex, especially when multiple applications are involved, as keeping track of changes, versions, and dependencies can be challenging, leading to conflicts that may impact cluster stability.

In the era of cloud data platforms, Snowflake has emerged as a market leader, revolutionizing the way businesses store, process, and analyze data. However, the true value of Snowflake lies not only in its cloud data warehousing capabilities but also in its robust data transformation features. These transformations are critical for turning raw data into actionable insights, fueling data-driven decisions.

Structured Query Language (SQL) remains the foundation of data engineering, enabling data analysts and professionals to design, build, and maintain scalable data pipelines. Despite the rise of modern technologies like Apache Spark and NoSQL databases, SQL’s declarative syntax and universal adoption make it indispensable in data engineering workflows.

In today's data-driven world, a robust enterprise data strategy (EDS) is essential for organizations aiming to leverage their data assets effectively. An EDS serves as a roadmap that outlines how data will be collected, organized, processed, and utilized to support business objectives. This blog delves into the key components of an enterprise data strategy, its significance, and best practices for implementation.

Hackers will never cease seeking out vulnerabilities to penetrate. To keep your SaaS platform unprotected is to keep your front door unlocked—it just needs one weak link for someone to break through. SaaS companies hold confidential data, which automatically makes them vulnerabilities waiting to be exploited. Just one breach could result in leaked data, losses, and eroded trust. Ignoring vulnerability risks is simply waiting for tragedy to unfold.

Tracing is a critical feature in N|Solid that allows developers to analyze application performance by visualizing the relationships between different processes through spans and traces. Without proper tracing, debugging performance issues becomes significantly more challenging, as developers lack visibility into how requests flow through an application. Tracing helps pinpoint bottlenecks, optimize request execution, and improve overall application efficiency.