Systems | Development | Analytics | API | Testing

Latency and throughput are the two most important metrics for API performance. If your API feels slow or struggles with heavy traffic, understanding these is your first step to fixing it. Latency: The time it takes for a request to go to the server and back (measured in milliseconds). Think of it as how quickly a single request is handled. Throughput: How many requests your API can handle in a second (measured in requests per second). It's about your system's capacity.

I don't have a degree in computer science, and it's okay if you don't either! Lots of us Rubyists don't have formal computer science credentials. So for a long time, I avoided learning about big-O notation. It looks a little too much like higher math. I mean just look at something like: O(N^2). Come on!

Observability isn’t a luxury, it’s a necessity. But for teams managing large Node.js applications, getting real-time visibility into performance, memory usage, async behavior, and security can be a complex and risky endeavor, especially if it means modifying your production code. That’s where N|Solid by NodeSource changes the game. Imagine getting deep, real-time insights into your Node.js applications without touching a single line of your business logic.

For too long, businesses have been adrift in a sea of static dashboards and colorful visualizations, mistaking activity for insight. They call it business intelligence, but in reality, it's just more noise. These legacy dashboards are inherently unintelligent; they might answer your first question, but they immediately force you to create ten more dashboards to get subsequent answers. It’s a vicious cycle of dashboard sprawl, not true intelligence.

Data is everywhere, but transforming it into clear, actionable insights is easier said than done. For organizations using Qlik, the platform offers a strong foundation for analysis, but how do you take your dashboards from functional to exceptional? Enter Vizlib from insightsoftware, the game-changing Qlik add-on that empowers users to turn basic dashboards into engaging data stories.

By Erez Schnaider, Technical Product Marketing Manager, ClearML The promise of artificial intelligence, particularly with the advent of LLMs, is transformative. Organizations are eager to harness this power, integrate AI into their products, and automate complex processes in order to materialize the lofty promises of generative AI – efficiency, deep domain knowledge, and a competitive edge.

The new bugsnag-kmp SDK is a unified error monitoring solution for Kotlin Multiplatform (KMP) projects, enabling developers to track and monitor errors across Android, iOS, and web platforms from a single codebase. The new SDK works seamlessly on Android, iOS, and web browsers Native Integration, each one linking directly with our existing platform SDKs.

Fivetran’s Connector SDK radically simplifies the process of building custom connectors for unsupported data sources.

To unlock AI’s full potential, organizations must combine strong data integration with human insight and operational alignment.

Choosing the right framework can significantly impact the success of a dev project. If you've chosen to use Python for your project's backend, you might need to decide between Flask and Django, the most popular web frameworks in the Python ecosystem. This article will help you determine which of the two best suits your specific needs by examining the key factors to consider. Before diving into the technicalities, let's briefly introduce both Flask and Django.