Operations | Monitoring | ITSM | DevOps | Cloud

Since I’m starting development with the dbRosetta database, and since I’m way more comfortable with databases than with code, I’m going to continue within the database sphere for a bit as we build out dbRosetta. My next step is to work with the AI to get a pipeline in place to take our database code and deploy it to Azure Flex Server.

Gartner IT Symposium/Xpo is always a standout experience for ScienceLogic, and this year’s event in Orlando was no exception. The event brought together seasoned IT leaders, analysts, and solution providers, creating a dynamic hub for meaningful conversations, hands-on demos, and translating future-driven insights into action. More than being honored to attend, ScienceLogic thrives on engaging with IT leaders on the show floor, in sessions, and throughout the event.

Simplify Kubernetes Deployment. Learn the difficult 6-step manual process for deploying Docker containers to Kubernetes, the friction of YAML and kubectl, and how platform tools like Qovery automate the entire workflow.

Observability in DevOps: Diagnose system failures faster. Learn how true observability differs from traditional monitoring. End context-switching, reduce MTTR, and resolve unforeseen issues quickly.

Many organizations struggle to track how their cloud infrastructure changes over time. Modern environments span tens of thousands of resources across hundreds of accounts and multiple clouds. Application teams add new services and regions at a rapid pace, increasing the number and variety of resources that need to be managed. These shifts can cause infrastructure configurations to drift from a well-architected state, increasing the risk of service reliability issues and unexpected cloud spend.

Leaders in every industry are investing heavily in AI. Shocking, I know. Operations teams are modernizing infrastructure and automating workflows while boards are asking for faster returns. And yet, for all the investment, one question still lingers: where’s the value? The truth is that most enterprises have a translation problem, not necessarily ‘just’ a visibility problem. Executives see AI as a growth strategy, but IT sees it as operational complexity.

In the era of cloud-native architectures, Site Reliability Engineering (SRE) has matured from a discipline focused purely on uptime to a sophisticated practice of efficient reliability. The key driver for this evolution is an undeniable truth: cloud spend has become intrinsically linked to system stability.

We benchmarked Diskless Kafka (KIP-1150) with 1 GiB/s in, 3 GiB/s out workload across three AZs. The cluster ran on just six m8g.4xlarge machines, sitting at <30% CPU, delivering ~1.6 seconds P99 end-to-end latency - all while cutting infra spend from ≈$3.32 M a year to under $288k a year. That’s a >94% cloud cost reduction. Extending Apache Kafka does come with an explicit tax.

On December 4th, 2025, we discovered an issue where the person shown as on-call on the dashboard didn’t match who was scheduled in the calendar. When we started investigating, we learnt that this only affected schedules with weekly rotations or weekly layers combined with custom timings.

Instead of giving you yet another chatbot, we built AI straight into the parts of Sentry where teams lose time, turning your existing data into instant context — and it’s now available to all Sentry users.