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And in case you’re wondering, yes, you could technically send data directly from your instrumented code to your desired Observability back-end sans Collector; however, this isn’t the best way to go about it. The Collector serves as a data pipeline which allows you to do things like mask, filter, transform, and append data before sending it to your Observability back-end. It can also ingest Metrics data from your infrastructure, like your Prometheus-style metrics generated by your Hashi stack or Kubernetes cluster metrics.
If you really want to up your SLO game (and you definitely should), you need to define your SLOs as code. This is where OpenSLO comes to the rescue! Originally started up by Nobl9, OpenSLO is an open-source “specification for defining SLOs to enable a common, vendor–agnostic approach to tracking and interfacing with SLOs.” Super dope. Don’t you just love standardization? 💜
Note: Although it’s outside of the scope of this post to dig deep into this topic, in case you’re curious, you can check out what an OpenSLO YAML definition looks like here.
Now, if you’re adding an OTel Collector to the mix, then it means that it needs to run somewhere. That somewhere can include Kubernetes, Nomad, or Virtual Machines (VMs) as a binary. Regardless of where you deploy it, you will need some sort of Infrastructure-as-Code (IaC) thing to configure and deploy the Collector. Luckily, you can use IaC tools like trusty ‘ole Terraform, superfly competitor Pulumi, oft-forgotten-yet-still-cool-kid Ansible. Or heck, write your own custom scripts to do it. It’s up to you. Just. Codify. It.
*Codifying the deployment of the OTel Collector *(to Nomad, Kubernetes, or a VM) using tools such as Terraform, Pulumi, or Ansible. The Collector funnels your OTel data to your Observability back-end. ✅