devops-exercises open source analysis
Linux, Jenkins, AWS, SRE, Prometheus, Docker, Python, Ansible, Git, Kubernetes, Terraform, OpenStack, SQL, NoSQL, Azure, GCP, DNS, Elastic, Network, Virtualization. DevOps Interview Questions
Project overview
⭐ 80503 · Python · Last activity on GitHub: 2025-12-27
Why it matters for engineering teams
Devops-exercises addresses the practical challenge of mastering a wide range of tools and concepts essential for modern infrastructure and site reliability engineering. It offers hands-on scenarios covering Ansible, AWS, Kubernetes, Terraform and more, helping engineers build real-world skills that are directly applicable to production environments. This open source tool for engineering teams is particularly suited to production engineers, SREs and DevOps specialists looking to deepen their operational knowledge and prepare for technical interviews. The project is mature and widely used, reflecting a reliable resource for continuous learning. However, it is not a turnkey solution for deployment or orchestration; teams seeking a production ready solution for managing live infrastructure should consider dedicated platforms instead.
When to use this project
Devops-exercises is a strong choice when teams want practical, scenario-based training to improve their operational skills and prepare for real-world challenges. It is less suitable when looking for a self hosted option for infrastructure management or automated deployment, where specialised tools would be more appropriate.
Team fit and typical use cases
Production engineers, SREs and DevOps professionals benefit most from this repository, using it to simulate common operational tasks and troubleshoot complex environments. It typically supports teams working on cloud infrastructure, container orchestration and continuous delivery pipelines, helping them build confidence with tools like Docker, Prometheus and Terraform in a controlled setting.
Topics and ecosystem
Activity and freshness
Latest commit on GitHub: 2025-12-27. Activity data is based on repeated RepoPi snapshots of the GitHub repository. It gives a quick, factual view of how alive the project is.