Systems | Development | Analytics | API | Testing

In this blog post we are going to look at using JMeter to support business testing in production. This is a slightly different topic to the one discussed in this post on testing in production. The one in the link above is around running your performance testing in production for reasons that are discussed in that post. This post is going to focus on how you can leverage your performance testing tools to support this activity, as discussed above we will focus on JMeter in some of the examples.

When you look at stories of great success like these, emotionally it’s tempting to feel like these companies just got the “unfair” advantage – that lucky, elusive edge that set them apart and put them ahead of their competition and into the hearts of their customers. But often, it is something else… In my role as Chief Strategy Officer at Qlik, I have been looking at this more formally with my team.

Accelerate data movement into the Snowflake Data Cloud to execute analytic workloads, AI and GenAI.

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.

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.