Operations | Monitoring | ITSM | DevOps | Cloud

How do you use ArgoCD and a GitHub workflow together with an external CI/CD tool (Jenkins in our case) to trace a specific PR/commit hash code in a GitHub repository that’s external to ArgoCD? The term ‘commit tracing’ comes from Michael Crenshaw – one of the lead developers of Argo.

The more organizations depend on collaboration solutions like Microsoft Teams for productivity, the more IT departments are expected to ensure a seamless experience every time. That demands more than just rapid troubleshooting when issues occur: it requires IT teams to get ahead of problems and keep them from affecting users in the first place. For that, synthetic testing is a must.

Few things hold IT teams back more than a lack of visibility. It’s exponentially harder to solve issues when they originate in parts of the environment you can’t see. That’s one of the big limitations of native tools for monitoring and managing Microsoft Teams. Microsoft Call Quality Dashboard, Admin Center and Service Dashboard, and Meeting Room Pro Dashboard are all constrained to the aspects of Teams that Microsoft controls directly.

An operations team at one of the Asia-Pacific’s largest managed service providers (MSPs) was drowning in their own success. Years of investment in monitoring tools and automation had created comprehensive visibility—and comprehensive chaos. Engineers opened dashboards each morning to find thousands of alerts waiting, with critical incidents buried somewhere inside. The scale of the problem was overwhelming their capacity to respond effectively.

If your legal department is anything like ours, you’re being inundated with requests from the business to use more and more AI tools. Whether it's developers wanting to use coding agents like Cursor, to security implementing AI-driven investigations, to sales and marketing leveraging AI for call insights and competitive research, we've seen a shift in what teams are trying and buying.

You deploy a fix late Friday and spend the weekend refreshing dashboards, hoping nothing breaks. You shouldn’t have to babysit a dashboard to know when something’s wrong. With the latest updates to Insights, you can now create alerts directly from any chart. Whether it’s a spike in 4xx errors after a deploy, a jump in P95 latency for an API endpoint, or a drop in throughput for a background job, you can set up alerts with just two clicks.

AWS Step Functions offers the Distributed Map state, enabling you to coordinate massively parallel workloads within your serverless applications. With this feature, a single Step Functions execution can fan out into up to 10,000 parallel workflows simultaneously, making it possible to efficiently process millions of items in parallel. This capability unlocks new possibilities for large-scale data processing, such as image transformation, log ingestion, or batch analytics.