Learn from production

• How KEDA’s Advanced HPA Tuning Cut Our App’s Latency by 96%

  Shailendra Singh

  This article describes how the authors fine-tuned KEDA’s advanced scaling settings (e.g. stabilization windows, scale-up policies) to reduce their P99 latency by 96%.

• Achieving High Availability with distributed database on Kubernetes at Airbnb

  Artem Danilov

  This case study shows how Airbnb runs a distributed SQL database across multiple Kubernetes clusters, coordinating node replacements, using custom operators, and isolating failures by availability zone.

• Scaling Real-Time Video on AWS: How We Keep WebRTC Latency Below 150ms with Kubernetes Autoscaling

  Oleksii Bondar

  This case study describes how they engineered Kubernetes autoscaling, regional routing, and efficient media paths to maintain end-to-end latency under 150 ms for live WebRTC video at scale.

• Production-Grade Pain: Lessons From Scaling Kubernetes on EKS

  Aditya Chowdhry

  This article walks through real challenges the team faced while scaling EKS — from autoscaling, ingress, DNS tuning to graceful shutdowns — and shares how they tackled each layer.

More case studies →

Matching jobs

1. • Data Engineer with NBC Universal

   • Salary: $100K to $130K a year

   • Location: based in the office (and remote from home) in New York, NY, USA

   • Tech stack: Kubernetes, AWS, GCP, Docker, SQL, Java, C++, Flink, Spark, Airflow

2. • DevOps Engineer with Booz Allen

   • Salary: $86.9K to $198K a year

   • Location: based in the office in Annapolis Junction, MD, USA

   • Tech stack: Kubernetes, Azure, Terraform, Cloudformation

3. • DevOps Engineer with Commerce

   • Salary: $99K to $172K a year

   • Location: remote from the United States of America

   • Tech stack: Kubernetes, AWS, Docker, Shell, Python, Scala, PHP, Ruby, Terraform, Ansible

4. • DevOps Engineer with Manulife

   • Salary: $110.53K to $205.27K a year

   • Location: based in the office (and remote from home) in Toronto, ON, CA

   • Tech stack: Kubernetes, Docker, Javascript, Java, Typescript, Terraform, Cloudformation, Jenkins, GitHub Actions

5. • DevOps Engineer with Shopmonkey

   • Salary: $165K to $185K a year

   • Location: based in the office (and remote from home) in Morgan Hill, CA, USA

   • Tech stack: Kubernetes, AWS, GCP, ArgoCD, Go, Shell, Typescript, Terraform, GitHub Actions, Gitlab

Discover more Kubernetes jobs on Kube Careers →

Build something

• Canary Deployments with Flagger

  Anuj Tyagi

  This tutorial shows how to use Flagger to roll out updates gradually (canary style) in Kubernetes, automating traffic shifts, metric checks, rollbacks, and webhooks.

• Building DC-DR Kubernetes Clusters with Karmada for Reliability and Seamless Failover

  Shaniul Shanto

  This tutorial shows how to set up geographically redundant Kubernetes clusters (DC + DR) using Karmada to achieve high availability, failover, and consistent deployment across clusters.

• Kubernetes Event Driven Autoscaling: Spring Boot & RabbitMQ

  AISSAM ASSOUIK

  This article shows how Kubernetes Event-Driven Autoscaling (KEDA) enables scaling of Deployments, StatefulSets, CRDs and Jobs based on external event sources (e.g. RabbitMQ queue length) rather than just CPU/memory.

• Service Binding for Kubernetes in Spring Boot cloud-native applications

  Saeed Zarinfam

  This tutorial shows how to use the Service Binding for Kubernetes spec to automatically bind Spring Boot apps to backing services (e.g. PostgreSQL) via Spring Cloud Bindings, eliminating manual env var wiring.

More tutorials →

More articles

• Kubernetes pod scheduling: balancing cost and resilience

  Phil Andrews

  This article explores how scheduling policies can optimize resource usage while preserving fault tolerance.

  It covers strategies for using anti-affinity, spread constraints, and affinity weights to balance cost and resilience.

• AI Infrastructure on Kubernetes

  In this report, you will learn how Kubernetes teams actually scale AI workloads based on 917 survey responses, revealing the gap between vendor capabilities and real-world GPU utilization challenges.

• How Kubernetes Pod Priority and Preemption Work

  Chaitanya Kharche

  This article explains how Kubernetes assigns priority to Pods with PriorityClasses, and how it can evict lower-priority Pods to make room for higher-priority ones when resources are tight.

• FastAPI at 200K Requests Per Minute: Horizontal Scaling with Gunicorn and Kubernetes HPA

  Hash Block

  This article describes how the author scaled a FastAPI service to handle 200,000 requests per minute using Gunicorn + Uvicorn workers and Kubernetes Horizontal Pod Autoscaler, with async endpoint optimization and autoscaling strategies.

Even more articles →