DevOps

Module 1: Foundations of DevOps Culture & Architecture

• The DevOps Paradigm: Core definitions, history, and breaking down traditional walls between Dev and Ops teams.
• Core Metrics & Frameworks: Understanding DORA metrics (Deployment Frequency, Lead Time for Changes, MTTR, Change Failure Rate).
• Socio-Technical Collaboration: Implementing CALMS principles (Culture, Automation, Lean, Measurement, Sharing).
• The Lifecycle Loop: Complete map overview from continuous planning and coding to deployment and monitoring loops.

Module 2: Advanced Version Control and Branching Strategies

• Git Internals: Deep architectural mechanics of Git objects, blobs, trees, commits, and refs headers.
• Branching Workflows: Selecting and configuring GitFlow, GitHub Flow, or high-velocity Trunk-Based Development.
• Conflict Mitigation: Advanced merge strategies, tracking rebase frameworks, stash trees, and commit cherry-picking pipelines.
• Repository Security: Setting up pre-commit hooks, automating secret scanning, and configuring rigid branch protection protocols.

Module 3: Continuous Integration & Continuous Deployment (CI/CD)

• Pipeline Automation Foundations: Structuring pipeline code patterns using GitHub Actions, GitLab CI, or Jenkins declarative syntax.
• Automated Testing Gates: Weaving linting checks, unit tests, code coverage matrices, and static code security scans (SAST) into transit.
• Artifact Management: Structuring secure build pushes out to container hubs or package registries (Docker Hub, Nexus, JFrog).
• Applied Lab: Engineering a multi-stage automated build-test-deploy pipeline triggered directly by pull requests.

Module 4: Containerization with Docker

• Container Theory: Exploring Linux namespaces and cgroups mechanics vs. traditional hardware virtualization hypervisors.
• Dockerfile Optimization: Writing efficient multi-stage build scripts to drastically reduce footprint sizes and minimize exposure matrices.
• Volume and Network Abstraction: Configuring runtime data persistence mappings and designing isolated container-to-container communication virtual networks.
• Multi-Container Layouts: Utilizing Docker Compose definitions to coordinate localized multi-service staging environments.

Module 5: Cluster Orchestration with Kubernetes

• Kubernetes Control Plane: Deep architectural dive into API Server, etcd cluster states, Controller Manager, and Scheduler nodes.
• Core Resource Declarations: Writing and deploying Pod definitions, ReplicaSets, StatefulSets, and internal/external Service endpoints.
• Configuration Management: Isolating application settings and database connection variables safely using ConfigMaps and secure Secrets.
• Ingress Routing: Configuring custom ingress controllers to balance traffic distributions and handle external public domain hooks safely.

Module 6: Infrastructure as Code (IaC)

• Declarative Provisioning: Understanding mutable vs. immutable architecture footprints utilizing HashiCorp Terraform.
• State Management: Configuring local vs. remote secure state lockouts using AWS S3 blocks or backend cloud databases.
• Modular Code Layouts: Building reusable, parameter-driven Terraform module blocks to deploy identical cloud environments rapidly.
• Configuration Drivers: Coupling Terraform with Ansible arrays to automatically handle OS-level provisioning and node security tuning.

Module 7: Cloud Computing Platforms (AWS Integration)

• Network Partitioning (VPC): Building custom virtual private clouds with isolated public and private subnet matrices, gateways, and routing sheets.
• Elastic Compute: Scaling EC2 instances manually or setting up auto-scaling groups with multi-zone Application Load Balancers.
• Identity Access Management (IAM): Structuring rigid access rules using the principle of least privilege across target user accounts and cloud roles.
• Managed Orchestration: Exploring serverless container runs via AWS Fargate or completely managed Kubernetes setups via Amazon EKS.

Module 8: Telemetry Monitoring, Logging, and Site Reliability (SRE)

• Metric Ingestion Arrays: Pulling server, cluster, and container hardware usage streams utilizing Prometheus architectures.
• Analytical Visualization dashboards: Building dynamic, real-time alert and tracking graphs inside Grafana workspaces.
• Centralized Log Storage: Aggregating system log file metrics using ELK/EFK stacks (Elasticsearch, Logstash/Fluentd, Kibana) or Loki structures.
• SRE Principles: Setting up clear Service Level Objectives (SLOs), Service Level Indicators (SLIs), and tracking error budget depletion lines.