Welcome to the 100 Days of Project-Based DevOps Learning Plan. This is a comprehensive roadmap designed for those who are ready to take their DevOps skills to the next level. By the end of these 100 days, you will gain expertise in advanced DevOps concepts, tools, and architectures, helping you stand out as one of the top 1% of DevOps Engineers in the world.
The 100 days are divided into various stages, each focusing on different aspects of DevOps and cloud-native technologies.
- Day 1–2: Deep dive into Domain-Driven Design (DDD) concepts and implement in a sample application
- Day 3–4: Study Event-Driven Architecture (EDA) and set up a basic event bus using Kafka
- Day 5–6: Explore CQRS (Command Query Responsibility Segregation) and Event Sourcing patterns; apply them to a service.
- Day 7–8: Learn about service mesh technologies; deploy Istio in a Kubernetes cluster and configure a sample microservices application.
- Day 9–10: Implement a saga pattern for distributed transactions in microservices.
- Day 11–12: Implement a multi-cloud architecture for an application, using AWS and Azure.
- Day 13–14: Explore and implement advanced Kubernetes concepts like Custom Resource Definitions (CRDs) and Operators.
- Day 15–16: Set up and configure a Spinnaker CD pipeline for multi-cloud deployments.
- Day 17–18: Implement GitOps for Kubernetes at scale using ArgoCD and Flux.
- Day 19–20: Automate Kubernetes cluster setup using Kubespray and validate with Sonobuoy.
- Day 21–22: Explore advanced Terraform features like modules, backends, and workspaces.
- Day 23–24: Automate multi-environment setups using Terraform and Ansible dynamic inventories.
- Day 25–26: Implement infrastructure testing using Terratest.
- Day 27–28: Explore Pulumi as an alternative to Terraform for IaC and implement a sample stack.
- Day 29–30: Study and implement cloud-native configuration management using Consul.
- Day 31–32: Implement advanced load balancing techniques using Envoy Proxy.
- Day 33–34: Study database sharding and partitioning at scale; implement for a high-volume application.
- Day 35–36: Implement and tune a Redis cluster for high performance and scalability.
- Day 37–38: Optimize a large-scale Elasticsearch cluster for search performance.
- Day 39–40: Conduct advanced performance tuning and load testing using tools like Gatling or Locust.
- Day 41–42: Implement advanced chaos engineering practices using tools like Gremlin.
- Day 43–44: Study and set up Service Level Objectives (SLOs) and Error Budgets for critical services.
- Day 45–46: Implement advanced monitoring and alerting strategies using Prometheus and Thanos.
- Day 47–48: Automate incident response using StackStorm for common operational scenarios.
- Day 49–50: Conduct a game day to simulate and handle real-world outages.
- Day 51–52: Implement a zero-trust network architecture in a cloud environment.
- Day 53–54: Automate compliance checks and remediation using tools like Chef InSpec or Open Policy Agent (OPA).
- Day 55–56: Set up and manage a Vault cluster for secrets management at scale.
- Day 57–58: Implement container security scanning and hardening using tools like Aqua Security or Sysdig.
- Day 59–60: Conduct a thorough security audit of the cloud infrastructure and applications.
- Day 61–62: Set up and configure Grafana Loki for log aggregation and analysis.
- Day 63–64: Implement distributed tracing at scale using tools like Zipkin or Jaeger in a complex microservices environment.
- Day 65–66: Explore and set up AIOps platforms for intelligent alerting and incident management.
- Day 67–68: Implement a business intelligence dashboard using tools like Tableau or Power BI connected to your application databases.
- Day 69–70: Analyze system logs and metrics to predict system failures using machine learning techniques.
- Day 71–72: Set up a Hadoop cluster and perform data processing with Hadoop MapReduce.
- Day 73–74: Implement a real-time data processing pipeline using Apache Flink.
- Day 75–76: Explore GPU-based computing for high-performance tasks and set up a sample application using AWS EC2 GPU instances.
- Day 77–78: Implement a serverless data processing workflow using AWS Lambda and AWS Step Functions.
- Day 79–80: Optimize a Spark cluster for large-scale data processing.
- Day 81–82: Implement a BGP-based multi-homing setup for high availability in network routing.
- Day 83–84: Set up a global load balancing service with Anycast IP addresses.
- Day 85–86: Configure advanced CDN strategies for dynamic content using AWS CloudFront and Lambda@Edge.
- Day 87–88: Implement a service mesh across a hybrid cloud environment.
- Day 89–90: Design and simulate a large-scale network using software-defined networking (SDN) tools.
- Day 91–95: Start a capstone project incorporating all the advanced concepts learned. This could be designing and implementing a scalable, secure, and highly available cloud-native application.
- Day 96–98: Finalize the implementation, focusing on optimization and best practices. Ensure all components, including networking, security, data processing, and observability, are finely tuned and integrated.
- Day 99: Present the capstone project to a group of peers or mentors, gathering feedback and insights.
- Day 100: Reflect on the entire journey. Identify key strengths, areas for improvement, and plan the next steps in your career. Celebrate your achievement and share your experience with the community.
At this point, you will have acquired in-depth knowledge and hands-on experience in advanced DevOps concepts. You will have the capability to design and manage complex cloud-native, highly scalable systems with confidence. Celebrate your journey and share your success with the community!
Feel free to contribute to this learning plan, whether it’s adding new resources, improving documentation, or suggesting projects. Open a pull request or submit an issue to get involved.
This project is licensed under the MIT License - see the LICENSE file for details.
For any queries or suggestions, feel free to reach out to me at Samsor Rahman Email.