Delivering quality software is as much about the people behind the code as it is about building the software itself.

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

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.

Fan expectations pose an existential threat to fan-centric businesses. And that's because what it means to be a fan has changed. Fandom used to be watching the Monday night game on TV or getting tickets for a concert when your favorite artist rolled through town.

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.

Gartner predicts that 70% of engineering teams will shift to "continuous quality" by 2026. But what is continuous quality, anyway?

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.

Check out Scout Monitoring's ollama-rails GitHub repo for samples on how to use ollama-ai to communicate with Ollama. Large Language Models (LLMs) have emerged as a game-changer, enabling machines to understand, generate, and process human language with unprecedented accuracy and fluency. One such tool that has gained significant attention is Ollama, a cutting-edge platform that allows developers to run LLMs locally without relying on cloud services.