Operations | Monitoring | ITSM | DevOps | Cloud

A customer asks you to onboard their firewall logs. Someone builds a Linux virtual machine at the customer edge. Someone else configures a syslog forwarder and tunes parsing on the appliance. That configuration then sits on a server your security operations team cannot reach. Multiply it by a hundred tenants and the manual labor becomes the business. For an MSSP, the right security data pipeline handles many tenants without multiplying that work. Few pipelines were built for it.

We’ve recently shown how Mobot, Sumo Logic’s AI assistant, has evolved from a search assistant into a true thinking partner, introducing natural language to log analysis and monitors. Today, we’re bringing that same conversational reasoning to one of the most powerful parts of the platform: playbooks in Automation Service. Historically, playbook authoring has been a highly manual process that required deep organizational and Sumo Logic platform knowledge.

Your pipeline works. The build passes, the image lands in the registry, and the job turns green. Then someone asks why production still runs the old version, and you spend the next two hours determining whether the problem is a deployment configuration, a runner, an approval rule, or the runtime itself. That is the wrong-tool tax.

Kubernetes normally excels at preserving uptime during maintenance tasks, but it’s not always perfect. Even something as benign as consolidating nodes after a traffic spike could take your application offline if not done carefully. This is where PodDisruptionBudgets (PDBs) come in. In this blog, we’ll explain why PDBs are important, what the risks are of not implementing them, and how you can find out which of your own deployments are missing PDB definitions. ‍

Microsoft Azure Monitor is an essential platform service for monitoring Azure resources. It provides valuable telemetry, logs, metrics, and alerts that help administrators understand the health of their cloud infrastructure. For organizations running Azure Virtual Desktop (AVD), Azure Monitor is usually the first monitoring service they deploy.

Perplexity was a big name a few years ago. But, we haven’t heard much out of them lately. There are plenty of posts on social media criticizing Perplexity to that effect. One thing that the posts miss is that Perplexity is still the king of AI search. Google is giving it a run for its money with the AI previews on Google search, but Perplexity still wins out in several measurable ways.

Somewhere in most production setups there is a container that stays awake all week to do one job on Friday. Maybe it re-indexes a database. Maybe it runs a batch report, or a data migration that only matters at month end. Either way, the resources are billed for seven days. Task containers close that gap. They are ephemeral, API-triggered containers that spin up inside an Upsun Cloud environment, run exactly one command, and remove themselves when that command exits. Nothing sits idle between runs.

Every pair of Monitoring Agents in your Obkio account create a Network Monitoring Session to monitor network performance between the two agents. Every one of those monitoring sessions comes with its own dedicated page, automatically. You'll find it under the Network Performance tab: click into it, pick a network monitoring session from the list, and the full page loads for that specific connection.

A log file full of raw text is close to useless when an incident is running. You can grep it. What you cannot do is ask how many failed logins came from one address in the last ten minutes. That is usually the question in front of you. Log parsing closes that gap, and a log parser is the software that does the work. In this blog, you will see: Log parsing is the process of reading a raw log line and extracting its values into named, structured fields.

During your last major outage, several monitoring tools raised alerts and every one of them was correct. What none of them could say was which alert explained the others, so the opening stretch of the incident went on assembling a picture the systems already held between them. That time shows up in your availability numbers, your SLA credits, and your board report. AIOps platforms close that gap by grouping the alerts caused by the same failure and handing your team one incident with context attached.