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

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).

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.

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.

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.

Logz.io alerts are a critical capability for our customers monitoring their production environment. By keeping a watchful eye for data that indicates an issue – like spiking memory metrics or 3xx-4xx response codes – alerting quickly notifies engineers that something is going wrong. Setting an actionable alert to immediately notify engineers of oncoming problems can be the difference between a minor issue and a major event with widespread customer impact.

The value of log files goes far beyond their traditional remit of diagnosing and troubleshooting issues reported in production. They provide a wealth of information about your systems’ health and behavior, helping you spot issues as they emerge. By aggregating and analyzing your log file data in real time, you can proactively monitor your network, servers, user workstations, and applications for signs of trouble.

We’re pleased to announce the beta release of BindPlane OP (Observability Pipeline), the first open source observability pipeline built specifically for OpenTelemetry. When we launched BindPlane in 2018, we set out to build a best-in-class observability management platform.

Cribl Packs are, in my opinion, our most exciting feature. Packs encapsulate the deep log processing capabilities and enable sharing of the best practices with customers, Worker Groups/Fleets, and the Community. Ease of sharing enables consistent configurations across distributed deployments of Cribl Stream or Cribl Edge. All users can leverage Packs–and should! If you collect Microsoft Windows Logs, use Palo Alto Networks or share logs via Syslog, Packs are for you.

Some popular monitoring tools in the market can complicate and create blind spots in your Elasticsearch monitoring. That’s why we made monitoring Elasticsearch simple, straightforward and actionable. Read along as we dive into the steps to monitor Elasticsearch using observIQ’s distribution of the OpenTelemetry collector. To monitor Elasticsearch we will configure two OpenTelemetry receivers, the elasticsearch receiver and the JVM receiver.