Want to improve your outlook with a splash of RUM? In our pursuit of connecting users to the right data at the right time, we’ve come to see Real User Monitoring as an invaluable tool for understanding the total picture when it comes to your web properties, apps, and cloud footprint. Do you find yourself asking any of these questions?

In Splunk Cloud Platform 8.2.2203, we're continuing to expand on interactivity capabilities and visualizations for Dashboard Studio. We've added the ability to use search results and job metadata as tokens, and pass tokens through drilldowns to other dashboards. There is a new map visualization for cluster maps and UI to match strings for dynamic coloring. And finally, we've included the ability to set a Studio dashboard as your home dashboard.

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

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).

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.