Systems | Development | Analytics | API | Testing

A balanced combination of manual and automated testing is the way to go if you want to achieve higher test coverage and efficiency. They cover for each other. Manual testing is easy to learn and do, but it is highly likely to create inconsistent test results, and sometimes it takes a lot of time and effort. On the other hand, automation testing requires a lot of technical expertise to create and maintain scripts, but it checks bugs much faster with higher consistency.

In this article, we'll talk about our experience implementing a design system at Kong. We'll go over the reasons why we decided we needed one in the first place, where we started, and how we got to where we are today. We'll also cover the technology we used and how it has transformed software development at Kong. Whether you have plenty of experience with design systems or are looking to get started with one, we hope you will find this article helpful and informative.

Data architecture is a structured framework for data assets and outlines how data flows through its IT systems. It provides a foundation for managing data, detailing how it is collected, integrated, transformed, stored, and distributed across various platforms. It also establishes standards and guidelines for data handling, creating a reliable and scalable environment that supports data-driven activities.

As the digital and physical worlds converge, the businesses of varied sectors are experiencing a seismic shift. While businesses are rapidly changing the ways how they function, however, the increasing customer demands and expectations, are making the need for this shift lightning-fast. Digital transformation in manufacturing industry is a significant trend that is taking place at a rapid pace.

Unlock real-time supply chain analytics with modern data infrastructure.

Debugging is a valuable skill for any software engineer to have. Unfortunately, most software engineers are not trained in it. And that's not just specific to developers going through boot camps; even in universities, we are not often taught and trained to use a debugger. My teachers and mentors were more interested in getting me to write programs rather than debugging them. If we are fortunate, debugging comes at the end of the semester, in a short, last session.

Building APIs is a fact of life for most modern developers. When we aren’t building them, we integrate them within our applications. Regardless of how you look at it, modern businesses are built on top of the foundation that APIs have built. They’re the bridges that allow different software systems to talk, share data, and work together seamlessly. But with great power comes great responsibility.

API testing is a vital part of the development lifecycle. In this comparison piece, you’ll compare Speedscale and Smartbear ReadyAPI and show where they stand in terms of API testing.

An operational data fabric focuses explicitly on integrating and managing disparate data in the context of operational systems and processes. It’s tailored to meet the needs of operational data management, often in real time or near real time, to support business operations. Let’s break down some key features of operational data fabric: To fully appreciate the transformative power of operational data fabric, it’s important to understand how it compares to data fabric.

Handling data in various formats is a common task in software, and CSV (Comma-Separated Values) files are among the most prevalent data formats you'll encounter. Whether for data migration, reporting, or simply importing and exporting data, processing CSV files efficiently is a necessary skill for any Ruby developer. In this article, we'll learn the practical aspects of parsing and handling CSV data using Ruby.