Operations | Monitoring | ITSM | DevOps | Cloud

There’s a crackle and spark in the air at Avantra lately, and I’m so pleased to be writing this bit on what we’ve accomplished with the Avantra 26 release. Automated root cause analysis, multi-tenant management support for Cloud ALM, enhanced security operations and financial operations monitoring BTP – it’s all there, and more. It’s an exciting and innovative release for Avantra!

On May 7th and 8th, 2026, Amazon Web Services (AWS) experienced an outage affecting Amazon Elastic Compute Cloud (EC2) in the dreaded US East 1 region. The original region of AWS located in Northern Virginia, us-east-1 or just “US East” as it is known, has been the subject of some of the internet’s most high profile and destructive outages and remains Amazon’s least reliable region.

The employee digital experience (DEX) is no longer just a concept; it has become a concrete discipline supported by specialized tools. At the center of this transformation is Collective IQ, Almaden’s DEX solution, available in the Essential and Business editions. The Business edition includes AlmaAI a family of generative AI capabilities that take IT management to a new level.

Luckily, in Kafka cache invalidation is not an issue, but we still need to name things. Let me tell you a story about naming things in Kafka.

Your Unreal game can ship with zero errors and still not feel great. Stutters during combat, a frame-rate cliff on the big boss, rubber-banding in multiplayer, none of it shows up as a crash and none of it shows up in Sentry, leaving you without any visibility into what your players are actually experiencing in the wild. Well, until now. Unreal Engine already gives you plenty of tools to measure game performance and collect runtime stats, but all that data stays on the dev’s machine.

In a recent webinar, The Journey to Production AI, Andre Elizondo walked through what separates a working agent demo from an agent worth trusting on a 2 a.m. page. Live polls during the session put numbers behind a pattern most platform teams already feel. ‍ ‍ Most teams are early. The ones who are further along did not get there by shipping a flashier demo. They got there by treating production AI as a platform problem.

Modern operations carry a quiet contradiction. Organizations have never had more data, more dashboards, or more instrumentation, yet teams increasingly struggle to gain a reliable sense of what the environment is actually doing. The problem is not the absence of information. It is the absence of bearings. This drift did not happen suddenly. It accumulated across years of transformation.

A battery is a complex electrochemical system where safety and revenue are decided in milliseconds. Cell temperatures, voltages, and state of charge change in real-time; dispatch decisions and thermal alarms must fire in real-time. Anything in between—your data pipeline, your historian, your alerting layer—has to disappear into the background.

When your database performance degrades, diagnosing the root cause is rarely quick or straightforward. Your existing tools might surface metrics like CPU utilization, wait events, and query duration, but then leave you to correlate the data and identify what went wrong. Worse, what first appears to be the root cause can often just be a downstream effect of multiple interrelated issues.

Today, roughly 94% of enterprises are projected to use some form of cloud service, with 82% adopting hybrid models that combine cloud and on-premises infrastructure. By 2030, around 60% of all corporate data is expected to be stored in the cloud, highlighting a major shift away from traditional storage systems toward distributed, on-demand infrastructure. With such a huge amount of data, companies need a scalable, affordable, and secure method to store 1 PB.