Systems | Development | Analytics | API | Testing

Ever written an automated test, only to have it fail the following day because the 'Submit' button changed its class name? Frustrating, I'm sure. Why are UI tests so flaky and why are selectors so flaky? Testers use CSS classes or IDs. Every time they see them, the class or ID changes every time the developers build the code. It makes automation brittle and long to update, especially due to the deep nesting and dynamic DOM elements. However, what if there was a more efficient way?

Note: A version of this article was originally published on CIO Influence. Burnout is widespread in the world of software engineering. According to a 2023 report, 73% of developers have experienced burnout at least once in their careers, and work-life balance has been cited as a key driver of developer satisfaction. Luckily, there are tools and strategies that teams can implement to reduce mental load, prevent burnout, and improve overall developer wellbeing.

Boosting testing efficiency, using AI wisely, and aligning testing with business goals can help you build a business that’s ready for anything.

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.