Operations | Monitoring | ITSM | DevOps | Cloud

The IT Operations space has a ton of recent advancements, and arguably the most elusive Observability unicorn for many organizations has got to be "AIOps," perhaps with "proactive monitoring" coming in as a close second place. You may be thinking.

Back in late May, we hosted a webinar on the heels of our Series D funding announcement where we also took the wraps off our big product announcement: Cribl Search.

As systems become more complex and distributed, the total amount of machine data put off by those systems continues to skyrocket. While teams may need access to an ever-increasing scope of machine data to gain insights into their increasingly complicated systems, that same need to access an increasingly large amount of data also creates cost concerns. Those concerns can grow into cost emergencies quickly.

From production monitoring to security concerns, businesses need to analyze and review their logs on a daily basis to make sure their system is up to par. Here are the reasons why analyzing your log data is so important. If you landed here, chances are you probably know what logs are, but we’ll start off with a short explanation of what it is.

Monitoring and analyzing log files to identify and resolve issues make up log observability. Log analytics is the process of extracting insights from log data. Logs are a valuable source of information for IT operations teams, as they provide insight into what is happening on a system or network. Logs can monitor system performance, troubleshoot problems, and identify security incidents. Logs are a vital part of application performance management.

RabbitMQ is one of the most popular open-source message brokers available. Its ability to be deployed in various configurations and on various platforms makes it a widely used tool; it also supports all major messaging protocols, making it very versatile. Still, debugging issues with a tool like RabbitMQ can be challenging, especially when it’s deployed on a large cluster. RabbitMQ logs are one way to go, as they help you backtrack to an earlier point while debugging.

Monitoring is often not the first thing on the mind of the modern developer. Yet, it’s necessary at many points of the software development lifecycle, including: before deprecating an API, before launching a new feature, after launching the feature, and more. In fact, monitoring needs can vary much more than the classic Ops monitoring. My podcast guest Liran Haimovitch is the co-founder and CTO of Rookout, a live data collection and debugging platform.

Software exists to make your job easier, not to suck the joy out of your work. It should be there when and if you need it, but be completely out of the way when you don’t — you’re at work to get a job done, not to use any particular product. If you’re forced into using the same underperforming, over-customized, difficult to implement, or just generally terrible software each and every day, it can really put a damper on the quality of your work and quality of life.

At Mezmo, we see a massive opportunity to reduce Mean Time to Detection (MTTD) and Mean Time to Resolution (MTTR) by making log data more valuable and actionable. Today, we’re thrilled to announce the release of the Mezmo Exporter for OpenTelemetry- the first step in our continued work with the project to further simplify the ingestion of log data and make that data more actionable with enrichment of key OpenTelemetry attributes.