Operations | Monitoring | ITSM | DevOps | Cloud

Cribl recently introduced Lakehouse, a powerful new feature within Cribl Lake that enables fast queries on the freshest data. But it’s so much more than just speedy searches. Lakehouse redefines how organizations collect, store, manage, and analyze telemetry data at scale, ensuring a future-proofed, cost-efficient, and flexible approach to data management.

Troubleshooting failed cron jobs without proper logging can be frustrating. Ubuntu cron logs record the execution of scheduled tasks, helping you identify what's working and what isn't. This guide covers what engineers need to know about Ubuntu cron logs – from finding them to analyzing their contents and setting up effective monitoring solutions.

Business process automation no longer sits on the sidelines. What was once an emerging technology is now the engine behind modern business operations. In fact, around 60% of companies already use automation tools in their workflows, according to Duke University. This is not just companies — developers are also contributing to this shift by adopting low-code, no-code, and digital process automation platforms. These new tools remove barriers that once slowed innovation.

There’s a problem in engineering culture that we don’t talk about enough: observability is an afterthought. It’s treated as tooling, not thinking. As a checkbox, not a habit. And that mindset gap creates real consequences: longer outages, frustrated teams and massive business costs. Atlassian’s Incident Management for High-Velocity Teams overview cites a 2014 study by Gartner, that the average cost of IT downtime is $5,600 per minute.

Monitoring doesn't always need to be complex. In this guide, we'll show you how to transform some raw logs into usable metrics using a lightweight, open-source setup. We'll also use the Telegraf agent to convert logs into Graphite metrics that you can easily visualize and alert on. This is ideal for system admins, DevOps beginners, or anyone interested in building more innovative monitoring pipelines from scratch.

When setting up an Elasticsearch cluster, one of the most common use cases is to ingest and search through logs. This blog post focuses on getting a benchmark that will tell you how well your cluster will handle your workload. It allows you to create a reproducible environment for testing things out. Do you want to change the mapping of something, drop some fields, alter the ingest pipeline?