Systems | Development | Analytics | API | Testing

BigQuery augmented analytics Table-Valued Functions (TVFs) automate insight discovery and pattern explanation using AI, ML and statistical methods.

In Under the Hood with Go TLS and eBPF, I left socket tracking as an exercise for later. The example used bpf_get_current_pid_tgid() and explicitly excluded concurrent TLS operations. Capturing plaintext was enough for that post. With rustls, later arrived: I could read the HTTP payload perfectly and still attach it to the wrong TCP connection. That’s a frustratingly convincing failure. The request looks right. The response looks right. The application works.

On its own, gen AI runs your performance analysis without a trusted data foundation, without your business context, and without built-in analytical methods, and that leaves its answers open to mistakes. Databox grounds AI tools in all three.

Every iOS team is about to hit a testing cycle they can't skip. And waiting until production is the expensive way to find out.

Qlik has been named a Leader in the IDC MarketScape: Worldwide Data Intelligence Platform Software 2026 Vendor Assessment. I'll say the obvious part first: we're proud of this. Independent recognition matters, and this one reflects work happening across every team at Qlik. But analyst recognition on its own doesn't help you run your data estate.

Apple's iOS 27 release marks another significant shift in the mobile landscape. Beyond new features for iPhone users, it signals a familiar pressure point for QA and development teams: the clock starts ticking the moment the update goes live, and your apps need to be ready. Here's why that matters: nearly 80% of iPhone users adopt the newest iOS version almost immediately. That high adoption rate leaves little room for error. If your app breaks on the iOS 27 release, your customers notice fast.

Continuous validation is redefining performance assurance, moving beyond traditional, one-off testing. By embedding ongoing validation into CI/CD pipelines, teams can identify and address regressions as soon as they arise. This approach reduces production incidents and enables faster, more confident releases.