Operations | Monitoring | ITSM | DevOps | Cloud

Imagine being able to automatically detect when a high-value order is placed, then log it and notify your sales team – without manually accessing your app code. Azure SQL Trigger Functions make this possible. By automating the response to database changes as they happen, you can streamline operations, sync data, and power workflows in near real-time. Azure SQL Triggers, especially when combined with serverless functions, offers a powerful, low-maintenance way to respond to real-time data changes.

The financial service industry has spent years modernising their software delivery pipelines. Build and test cycles are fast, infrastructure is automated, and engineering capability is no longer the bottleneck. The slowdown now occurs at the end of the process: the last mile, where a change must prove it is safe before it can enter production. This final step is governed by a trust layer with people in it.

Start with distributed tracing to find which service is slow, then use continuous profiling to see why the code is slow, and finally apply high-cardinality analysis to identify which users or conditions trigger the problem. It's 2 AM. Your phone buzzes. Users are reporting timeouts. The metrics dashboard shows p99 latency spiking from 200ms to 4 seconds, but everything looks normal—CPU at 60%, memory stable, no error spikes. A quick pod restart helps briefly, then latency climbs right back up.

In my previous posts, we discussed the where and the how of managing your ML assets. We showed you how JFrog Artifactory acts as a powerful, universal model registry (the “where”) and how the FrogML SDK serves as the gateway to get your models and metadata into it (the “how”). Now, let’s talk about the why.

Stop guessing on AI hardware. This DevOps guide details when to use TPUs vs. GPUs for optimal performance, cost, and framework compatibility in MLOps.

Traditional testing methods often delay the software development lifecycle, as we have grown used to these outdated processes without considering alternatives. Ephemeral environments introduce a more efficient solution. They allow for the quick creation and dismantling of isolated testing environments. These isolated environments approach leads to faster and more productive development cycles while still delivering high-quality software to users.

We look at the practical shifts unveiled at AWS re:Invent 2025 and how they’ll influence AI, networking, and cloud strategy heading into 2026. Another AWS re:Invent is behind us, and this year felt different.

5G continues to transform the telecommunications landscape, enabling massive device density, edge computing, and new enterprise use cases. However, operators still face significant cost pressures: from accelerating RAN modernization and 5G SA rollouts to energy demands and the shift to cloud-native network functions (CNFs). As telcos redesign their infrastructure strategies, open source has become a key lever to reduce costs, increase flexibility, and accelerate innovation.