Operations | Monitoring | ITSM | DevOps | Cloud

Most security stacks only move when told to. They wait for known IOCs, hunt for pre-defined suspicious strings, and trigger automation only after a condition lights up. By then, attackers have already pivoted. Agentic AI rewrites the rules. Instead of signature-based detection, it monitors behavioral baselines and identity signals, watching for violations of expectations formed from observed context.

Setting up and scaling observability across large, distributed environments often requires platform and SRE teams to coordinate access to infrastructure hosts and switch between configuration management tools and product-specific documentation. These tasks increase setup time and create delays in establishing visibility of critical services in Datadog. As teams expand their infrastructure, they need to coordinate Datadog configuration changes in a consistent and auditable way.

Continuous profiling has established itself as core observability practice, so much so that we’ve referred to it as the fourth pillar of observability. But despite the capabilities and growing adoption of continuous profiling, it can still be confusing to approach profiling as a newcomer and correctly apply it to different troubleshooting scenarios.

Operating Cilium at a small scale is straightforward. You install the Helm chart, choose a routing mode, and apply a few network policies. Day 1 is about getting packets to flow. Day 2 is about keeping them boring. At Datadog, we run Cilium across hundreds of Kubernetes clusters, tens of thousands of nodes, and hundreds of thousands of pods in multiple clouds. When operating at this scale, small configuration choices stop being minor details and start becoming risk multipliers.

Netdata has an incredibly powerful alerting engine. But this can sometimes be a double-edged sword: the flexibility to build incredibly specific, intelligent alerts is immense, but mastering its syntax can feel like learning a new language. We’ve heard this from so many of you. You tell us that configuring alerts is often the steepest part of the learning curve, a task that falls to the one “Netdata expert” on the team who has spent the time digging through the documentation.

This year-end wrap-up covers topics from BGP security (including ASPA and excessive AS-SETs) and the geopolitical (Ukraine’s IPv4 exodus, the Iran internet shutdown, and Red Sea cable cuts) to the year’s most significant outages (TikTok, the Spain/Portugal blackout, and cloud failures at AWS, Azure, and Cloudflare). Plus, we explore Starlink’s new Community Gateways, and revisit the evolving landscape of AS ranking and OTT service tracking.

By Bill Balnave, VP of Customer Success at Mezmo The core promise of modern observability is simple: cut Mean Time To Resolution (MTTR). Yet, despite a boom in tooling and investment over the last four years, the data tells a sobering story: our industry is actually getting worse at finding and resolving issues. Dashboards, once our trusted guide, have become the starting point for a chaotic "dashboard hunt" that rarely leads to the definitive root cause.

This blog post is based on Florent Huck, Developer Advocate at Upsun, at SymfonyCon 2023. We utilized AI tools for transcription and to enhance the structure and clarity of the content. The journey from a single monolithic application to a multi-application architecture doesn't have to be daunting. At a recent developer conference, Florent from Upsun's Developer Relations team shared a practical step-by-step guide on how to refactor a monolith into multiple applications using Upsun.

For years, platform teams have known what a service mesh can provide: strong workload identity, authorization, mutual TLS authentication and encryption, fine-grained traffic control, and deep observability across distributed systems. In theory, Istio checked all the boxes. In practice though, many teams hit a wall. Across industries like financial services, media, retail, and SaaS, organizations told a similar story. They wanted mTLS between services to meet regulatory or security requirements.

Digital Employee Experience (DEX) refers to how employees interact with the digital tools, systems, and technologies they use at work-and how those interactions affect their productivity, satisfaction, and overall work experience. DEX encompasses the quality of the digital interactions and services that employees encounter while using workplace technologies. It includes various factors such as application performance, network connectivity, device usability, and overall user satisfaction.