Hope you’re all doing well and staying warm today. A fun collection of articles this week with a little bit for everybody. Sit down, grab your favorite hot beverage, and enjoy! ☕
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From The Community
An excellent article from Salesforce engineering, covering their more popular design choices for building observable services.
Neat little weekend project for reading RF-transmitting utility meters and tracking the data in familiar observability tools.
A look at some of the differences between SRE and DevOps principles, with a particular emphasis on service levels and monitoring signals.
A/B testing is a great way to evaluate changes in your application, but it can become a chore to maintain the constantly evolving tests. Preply shares how they’ve automated their stack to enable development teams to more easily monitor and alert on their own test failures.
I never get tired of reading about Netflix teams and their custom data pipelines. An interesting read about how they perform auto-diagnosis and remediation across their infrastructure.
Last year, Uber released Orbit, an open source Python package for Bayesian time-series analysis and forecasting. This article covers all the updates and changes in their recent v1.1 release.
Fantastic article from Fred Moyer, one of our friends in the Monitoring Weekly community, on the importance of accuracy in our data with some tooling recommendations.
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This looks like a genuinely interesting tool for monitoring crashes in your Kubernetes cluster, with native hooks into Slack. Very cool.
Datadog always does a great job with these “which metrics to monitor” posts, and this one is no different. If you’re using Azure SQL, this is a great starting point for learning which metrics you should be keeping an eye on.
A guest post on the Grafana blog demonstrating Grafana Tempo and Linkerd with NGINX Ingress Controller for tracing requests through their request stack.
A surprisingly comprehensive compilation of just about every Top-like utility in the known universe. There were quite a few I’d never heard of here. o_O
A sample GitOps workflow for standing up Grafana Loki with Promtail in your Kubernetes cluster.
“Orbit is a Python package for Bayesian time series forecasting and inference. It provides a familiar and intuitive initialize-fit-predict interface for time series tasks, while utilizing probabilistic programming languages under the hood.”
“kwatch helps you monitor all changes in your Kubernetes(K8s) cluster, detects crashes in your running apps in realtime, and publishes notifications to your channels (Slack, Discord, etc.) instantly”
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See you next week!
– Jason (@obfuscurity) Monitoring Weekly Editor