Operations | Monitoring | ITSM | DevOps | Cloud

Earlier this month, we shared exciting news with our first placement in the 2022 Gartner® Magic Quadrant™ for Application Performance Monitoring and Observability: we are in the Visionary Quadrant. This research is near to my heart, as I led this research for four years; so, I wanted to reflect on why this is an accurate placement for Logz.io. The Visionary Quadrant is designated for those organizations who are pushing the boundaries of a specific market and technology.

It’s common for most CISOs to lead off a security conversation by comparing what other companies in the industry are spending on cybersecurity and simply matching that. After all, regardless of the results, the CISO can always tell the board of directors they’re following industry guidelines around security budgets. The problem is security outcomes are bad regardless of budgets. It’s not what you spend. It’s the results you get that matter.

Armed with a Ph.D. from UC San Diego, our guest started off with internships at Google and Microsoft before gaining valuable experience as a VP and a highly sought-after consultant for startups and SMBs. Now he’s one of the world’s foremost experts on wrangling vast data sets and maximizing efficiency.

Many years ago, I attained my private pilot’s license. This entailed completing a very structured program, similar to how most companies introduce a product to a new user. Let’s be honest, there is a really good reason for this – to avoid the crash and burn. With flight training, it’s literal, while with products it’s a bit more figurative (except when you YOLO something into production–that can cause a crash and burn–and leave for a bad first impression).

Infrastructure as Code (IaC) is the cloud-computing practice of putting the provisioning and configuring your cloud resources into machine-readable code.

As a software engineer running applications in production, it is essential to monitor this environment to maintain the health of your applications. Production monitoring software and systems are used to improve observability so that you can better understand your operating environment and visualise performance issues easily.

In this post, we will address the common monitoring use case of filtering metrics within the observIQ OpenTelemetry (OTEL) collector. Whether the metrics are deemed unnecessary, or they are filtered for security concerns, the process is fairly straightforward. For our sample environment, we will use MySQL on Red Hat Enterprise Linux 8. The destination exporter will be to Google Cloud Operations, but the process is exporter agnostic.