Systems | Development | Analytics | API | Testing

With the rapid expansion of AI adoption, data center construction is accelerating around the world. Behind this boom, however, lies a growing concern: a serious shortage of electric power, as supply struggles to keep pace with soaring demand. Nowhere is this issue more visible than in the United States.

Running real integration tests for iOS projects is one of those problems that sounds straightforward until you're actually in it. The core tension: your backend runs on Linux, your iOS app can only build on Apple hardware. The two worlds don't meet naturally. Most teams end up mocking server responses in their mobile tests to isolate components without relying on backend services.

The future depends on a unified data architecture that can support everything from analytics and reporting to AI agents.

Templates in BigQuery Studio notebook gallery, now GA, help you bypass the setup phase and jump straight into discovery.

Slow database queries are really a pain. It’s easy to blame Django for taking ages to process a request, but the real issue may lie in an SQL query not doing what it should be. These performance degrading queries are often hiding in plain sight. By the time you notice them, they are already affecting your end users. AppSignal is an application performance monitoring (APM) tool with support for Python and Django out of the box.

Most real estate platforms feel they have a solid foundation with their existing data: listings, photos, pricing, and descriptions. This data is a powerful starting point. However, the real opportunity lies in transforming this raw input into genuine intelligence for critical decisions—like optimizing property pricing, refining search rankings, or personalizing listing recommendations. Raw listing data is the necessary input; adding context is the key to unlocking its massive, untapped value.

AI coding tools have quickly become part of everyday software development. What started as an experiment is now a normal part of how many teams write, ship, and update code, with tools like GitHub Copilot, Cursor AI code editor, Anthropic’s Claude, and ChatGPT being used across a wide range of workflows. The productivity gains are real. Developers are moving faster, shipping more changes, and spending less time on repetitive work. But as output increases, so does the risk.

Ask any enterprise architect about load testing platforms, and you’ll hear plenty about horsepower, integrations, and feature lists. Yet, the reality is often counterintuitive: the most powerful tool isn’t always the right one. Over the years, I’ve seen teams exhaust budgets or miss critical performance issues by reaching for the so-called “Swiss Army knife” of testing, only to discover too late that they needed a precision instrument – or the reverse.