Operations | Monitoring | ITSM | DevOps | Cloud

Developers have long used profilers to diagnose performance bottlenecks and improve the efficiency of their code. But a modern version of profiling, continuous profiling, is quietly redefining what profiling is and what it can do. By running nonstop in production with very low overhead, continuous profilers give teams always-on visibility into how their code behaves in the real world.

Great data is a prerequisite to all things AIOps and observability. Great observability data results in fewer observability gaps, better analysis and insights, and more confidence within teams that rely on the power of modern AIOps and observability technologies. Goals for improved automation, IT efficiencies, intelligent triage and remediation all become more achievable with better data.

When you manage complex IT infrastructure, it becomes critical to use tooling to understand what’s happening across all of your systems in terms of performance, reliability, and compliance. Monitoring key indicators manually is simply no longer possible at that scale. Puppet has long been known as a solution for managing large environments and collecting a vast amount of data about your infrastructure, but accessing and visualizing that data in a meaningful way can be a challenge.

If you’re unfamiliar with AWS Summits, these are conferences that occur on a yearly basis in different cities. The events are mostly used to announce new products and technologies. This year, the theme was AI, as evidenced by the keynote, a large majority of the talks, and a walk around the vendor floor. The keynote talk was hosted by Swami Sivasubramanian, VP of Agentic AI at AWS.

SAP Customer Experience (CX) has undergone a remarkable transformation over recent years, evolving from fragmented monitoring to a scalable, automated observability powerhouse. In a recent fireside chat, Martin Norato Auer, SAP CX’s VP of Observability, shed light on the strategies, practices, and measurable impacts behind SAP’s SLA, uptime, and responsiveness achievements.

AI is being used to automate just about everything these days, from writing code to making coffee. Observability is no exception. But before we dive into how AI can actually help, it is worth stepping back to look at what already works, what does not, and where the real gaps are.

You’ve built something amazing. Your services are scaling, your users are happy, and your team is shipping code like never before. Then the cloud bill arrives, and one line item makes your eyes water: observability. That Datadog invoice feels less like a utility bill and more like a ransom note. It’s a modern engineering paradox. The tools that give you sight into your complex systems are the same ones that can blind you with runaway costs.

You're running Kafka on Confluent Cloud. You care about lag, throughput, retries, and replication. But where do you see those metrics? Confluent gives you metrics, sure, but not all in one place. Some live behind a metrics API, others behind Connect clusters or Schema Registries. You either wire them manually or give up. What if you could stream those metrics to a platform built for high-frequency, high-cardinality time series, and do it in minutes?