Operations | Monitoring | ITSM | DevOps | Cloud

Picture this: Your dashboards are calm. CPU load is steady. Error rates are low. Everything looks fine. That is, until the alarms go off. Now what? Metrics tell you something’s wrong, but not what, where, or why. They reveal symptoms, not root causes, and in high-stakes environments, that’s only half the story. Say your API response times spike. Metrics raise the flag, but they don’t tell you if it’s a code deployment, a database hang, or a traffic surge.

Elasticsearch is a distributed search and analytics engine that powers everything from log management platforms to e-commerce search bars. It excels at indexing and retrieving large volumes of data quickly, but like any complex system it can slow down under heavy load or inefficient queries.

As organizations modernize, most applications don’t fit neatly into one category—they span both traditional three-tier architectures and cloud-native microservices. To monitor these hybrid environments effectively, teams need APM tools that can seamlessly connect the two worlds.

In 2025, microservices are everywhere and Kubernetes is the de facto standard for operating cloud native applications. But not all apps are built in microservices architectures. For most enterprises, hybrid environments are the reality, with their business run on a mix of three-tier and cloud native applications.

I tried thinking back to when the last time I read an actual tutorial that did not include a bunch of em (—) dashes, semicolons, normal dashes, and an unnervingly large quantity of the phrases like “XYZ-thing Alert ” and “Exciting News!”. Well, hold on to your suspenders folks, here we go again. Part 2 is up and it’s a controversial one.

AI agents for SREs were supposed to slash mean time to resolution and eliminate alert fatigue. Instead, most teams got expensive, unreliable tools that burn through tokens without delivering insights. But what if the problem isn't the AI models themselves? Recent benchmarking reveals the real bottleneck: context engineering. When we tested our context engineering approach against conventional methods, the results were dramatic: Scroll down for our benchmark results to see the full comparison.

Your security teams face escalating data volumes, vendor changes, and cost pressures when they migrate between SIEM platforms. Cribl simplifies these migrations by giving you flexible data routing, reducing storage costs, and accelerating time-to-value. How? Let’s look at how a global customer used Cribl Stream to migrate CrowdStrike FDR logs from Splunk to Microsoft Sentinel efficiently and cost-effectively.

Every day, IT teams are flooded with alerts—thousands of messages about performance issues, service outages, or suspicious activity. With so many notifications, it’s easy to get overwhelmed, miss critical problems, or waste time chasing false alarms. Correlating related alerts into groups can help reduce the noise and make sense of everything, but setting up those correlations takes time, experience, and a lot of both system and historic knowledge.