The article "Autoscaling with Keda and Prometheus Using Custom Metrics in Go" on *Medium* provides a detailed guide on how to implement autoscaling in Kubernetes using Keda and Prometheus. It demonstrates creating custom Prometheus metrics in a Go application, deploying it on Kubernetes, and configuring Prometheus to scrape these metrics. The article then shows how to integrate Keda with Prometheus to scale pods based on custom metrics, such as the number of HTTP requests or product orders, ensuring dynamic resource allocation during varying traffic conditions.


https://medium.com/vakifbank-teknoloji/autoscaling-with-keda-and-prometheus-using-custom-metrics-in-go-558a64668fc4

The blogpost highlights potential security risks associated with automating Terraform lifecycle management. It discusses how malicious actors can exploit vulnerabilities in Terraform automation platforms, such as Hashicorp Cloud and Atlantis, by creating custom providers or using data sources to execute malicious code during the terraform plan phase. This can lead to unauthorized access to sensitive cloud credentials, compromising entire cloud environments. The article emphasizes the need for secure defaults and validation mechanisms in these platforms to mitigate such risks

https://snyk.io/blog/gitflops-dangers-of-terraform-automation-platforms/

An operator to manage ephemeral Kubernetes resources 🐝

https://github.com/NCCloud/mayfly

OpenTofu / Terraform / Terragrunt and Atmos version manager

https://github.com/tofuutils/tenv

In his article "TTR: the out-of-control metric," Lorin Hochstein critiques the application of the Time-to-Resolve (TTR) metric in incident management. He argues that since incidents represent periods when systems are out of control, applying statistical analyses to TTR is ineffective and does not lead to meaningful improvements.

https://surfingcomplexity.blog/2024/11/23/ttr-the-out-of-control-metric/

Richard Artoul explores the distinctions between "shared nothing" and "shared storage" architectures, particularly within data streaming contexts. He highlights how shared storage systems, by decoupling data from metadata, offer enhanced flexibility and scalability compared to traditional shared-nothing models. citeturn0search0
https://www.warpstream.com/blog/the-case-for-shared-storage

The mighty, self-hostable Git server for the command line🍦

https://github.com/charmbracelet/soft-serve

Runbook automation platform with deep observability integrations for SRE & On-Call Teams

https://github.com/DrDroidLab/playbooks

In the blogpost examined how increasing CPU utilization can lead to higher latency, affecting overall system performance. Through various experiments, they observed that as CPU usage rises, latency increases, highlighting the importance of optimizing system efficiency to maintain performance under varying loads. citeturn0search0

https://github.blog/engineering/architecture-optimization/breaking-down-cpu-speed-how-utilization-impacts-performance/

In this article "Solutions to the Lost Update Problem," Sönke Ruempler addresses the challenges of concurrent transactions leading to lost updates in database systems.

https://ruempler.eu/2024/11/25/solutions-to-the-lost-update-problem/

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Define sleep & wake up cycles for your Kubernetes resources. Automatically schedule to shutdown Deployments, CronJobs, StatefulSets and HorizontalPodAutoscalers that occupy resources in your cluster and wake them up only when you need them, reducing that way the overall power consumption.

https://github.com/rekuberate-io/sleepcycles

Chaos snake for Kubernetes. Chaos engineering combined with a classic snake game.

https://github.com/deggja/chaossnake

Lawrence Jones discusses the challenges of ensuring that an incident management system can alert its own team during outages.

https://incident.io/hubs/building-on-call/who-watches-the-watchers

In the article discusses the challenges of maintaining Service Level Objectives (SLOs) in a microservices environment. The team redefined their Critical User Journeys (CUJs) and implemented end-to-end (E2E) testing to automate SLO maintenance, resulting in a 99% reduction in maintenance time and immediate impact assessment during incidents.

https://engineering.mercari.com/en/blog/entry/20241204-keeping-user-journey-slos-up-to-date-with-e2e-testing-in-a-microservices-architecture/

Local development for AWS Lambda's and Terraform

https://github.com/uatec/teleform

NVIDIA device plugin for Kubernetes

https://github.com/NVIDIA/k8s-device-plugin

In December 2024, AWS introduced a visual deployment timeline feature for CloudFormation, enhancing the infrastructure-as-code service with real-time visualization of resource provisioning sequences. This timeline offers a graphical representation of the order and duration of resource deployments, providing insights into dependencies and potential bottlenecks.

https://www.infoq.com/news/2024/12/cloudformation-visual-deployment/

In the article "The Karpenter Transformation," Nadav Buchman from Fiverr Engineering discusses the company's migration of their Kubernetes compute nodes to Karpenter, an open-source Kubernetes node lifecycle manager developed by AWS.
https://medium.com/fiverr-engineering/the-karpenter-transformation-1c278294bd9b

Intelligence for Kubernetes. World's most promising Kubernetes Visualization Tool for Developer and Platform Engineering teams.

https://github.com/KusionStack/karpor

Automate Kubernetes Configuration Editing

https://github.com/kptdev/kpt