9. Infrastructure as Code
Terraform
• Providers, resources, variables, modules, workspaces
• AWS, Azure, K8s provisioning
• Terraform Cloud
Lab: Deploy AWS EC2 with VPC using Terraform
Project: Full IaC setup for K8s cluster with Terraform + Helm

10. DevOps in Cloud Pipelines
Azure DevOps
• Repos, Pipelines, Artifacts, Releases, Boards
AWS CodePipeline
• CodeCommit, CodeBuild, CodeDeploy orchestration
• Manual approvals, rollback
Lab: Deploy app via Azure DevOps YAML pipeline
Project: CI/CD pipeline on AWS using CodePipeline for Dockerized app

11. ChatOps and Collaboration
ChatOps (Slack/MS Teams + Bots)
• Integrate Jenkins, GitHub, Argo CD, and Prometheus alerts
• Build your own chatbot with Webhooks/API
Lab: Configure Slack notifications for Jenkins/Argo CD
Project: Trigger deployment via Slack command using Webhooks

Capstone Projects (Choose 1–2)
• Enterprise-grade CI/CD pipeline using Jenkins, Docker, K8s, Argo CD
• GitOps deployment with Vault-secured secrets
• Real-time monitoring/logging setup with ELK + Prometheus + Grafana
• Cloud-native deployment using Terraform + AWS CodePipeline

Course Duration
• Lab Exercises: 30+
• Real-World Projects: 3–5

1. Git, GitHub, GitLab, Bitbucket
• Maintain, version, and manage infrastructure and application code
repositories.
• Create and manage branching strategies (Git Flow, trunk-based).
• Implement PR (Pull Request) workflows with code reviews and approvals.
• Integrate repositories with CI/CD tools (Jenkins, GitHub Actions, etc.).
• Set up and manage Git webhooks and GitOps triggers.
• Enforce code quality and commit conventions.

2. Jenkins (CI/CD Automation)
• Configure Jenkins masters, nodes, and pipeline libraries.
• Create freestyle and declarative pipeline jobs for build, test, and deploy
stages.
• Integrate Jenkins with GitHub, Bitbucket, Nexus, SonarQube, and Docker.
• Secure Jenkins with role-based access and credentials.
• Automate artifact packaging and deployment with parameters.
• Schedule pipelines with triggers (SCM, cron, webhook).
• Store and manage credentials using Jenkins Credentials plugin or Vault.

3. Docker
• Create Dockerfiles to containerize applications and services.
• Build, tag, push, and pull Docker images from Docker Hub or private
registries.
• Optimize multi-stage Docker builds for performance and size.
• Manage container volumes, networks, and health checks.
• Use Docker Compose to run multi-container applications.
• Scan images for vulnerabilities and harden containers.

4. Kubernetes
• Create and manage Kubernetes resources (Pods, Deployments, Services,
etc.).
• Configure Ingress, ConfigMaps, Secrets, and RBAC policies.
• Implement readiness/liveness probes and autoscaling.
• Monitor workloads and troubleshoot with kubectl, k9s, and logs.
• Automate cluster deployments via Helm or Argo CD.
• Manage namespaces, node affinity, and resource quotas.

5. HashiCorp Vault
• Store and retrieve dynamic secrets and credentials securely.
• Create Vault policies, tokens, and namespaces.
• Enable secret engines (KV, AWS, database) and dynamic secrets.
• Integrate Vault with Jenkins, Ansible, and Kubernetes.
• Automate secret rotation and access audit.

6. ELK Stack (Elasticsearch, Logstash, Kibana)
• Configure Logstash pipelines to parse application and infrastructure logs.
• Deploy Elasticsearch clusters and fine-tune indexing and retention.
• Build Kibana dashboards for real-time log visualization.
• Integrate logs from Docker/Kubernetes using Beats or Fluentd.
• Set alerts and filters for critical logs and anomalies.

7. Argo CD (GitOps)
• Deploy applications declaratively from Git repositories to Kubernetes.
• Manage Argo CD apps via CLI/UI/API.
• Use sync policies and hooks to control release timing.
• Integrate Helm or Kustomize into Argo pipelines.
• Monitor application status and auto-heal via Git state reconciliation.

8. Jira
• Track development and infrastructure tasks using Agile boards.
• Link CI/CD activities and commits to Jira issues.
• Use Jira automation rules to trigger workflows or alerts.
• Create change requests and document deployment runbooks.
• Generate release notes and audit reports.

9. Nexus Repository Manager
• Store and manage build artifacts (JARs, WARs, Docker images).
• Configure Maven/Gradle repositories and proxy upstreams.
• Implement cleanup policies for old builds and snapshots.
• Manage access controls and credentials.
• Integrate Nexus with Jenkins or GitLab CI/CD.

10. Helm
• Create Helm charts to package Kubernetes applications.
• Use templating and values files to manage environment-specific
configurations.
• Publish and pull charts from Helm repositories.
• Integrate Helm with GitOps workflows (Argo CD or Flux).
• Perform dry-runs and rollback deployments.

11. Ansible
• Automate server provisioning and software installations.
• Write reusable playbooks and roles for different environments.
• Manage inventory (static/dynamic) and host variables.
• Integrate Ansible with Jenkins, Git, and Vault.
• Use Ansible Tower/AWX for RBAC and job scheduling.

12. Terraform
• Define cloud infrastructure (AWS, Azure, GCP) using HCL.
• Use modules and workspaces for reusable, multi-environment IaC.
• Manage Terraform state remotely (S3, Azure Blob, Terraform Cloud).
• Automate Terraform pipelines with Jenkins/Azure DevOps.
• Integrate with Vault for secure secrets injection.

13. Vault (Secret Management)
• (Also covered under HashiCorp Vault above)

14. Prometheus
• Collect metrics using exporters (Node Exporter, kube-state-metrics, etc.).
• Configure PromQL alerts and thresholds.
• Monitor app performance, container metrics, and resource usage.
• Integrate with Grafana for visualization and alerting.

15. Grafana
• Create dashboards from Prometheus, InfluxDB, Loki, etc.
• Set up templated dashboards for different environments.
• Configure alert channels (email, Slack, Teams).
• Use provisioning as code for dashboard and datasource automation.

16. Azure DevOps / AWS CodePipeline
Azure DevOps:
• Manage build and release pipelines using YAML and Classic Editor.
• Use Azure Repos and Artifacts.
• Implement gated check-ins and approvals.
• Deploy apps to Azure AKS, Web Apps, or VMs.
AWS CodePipeline:
• Create CI/CD pipelines for Lambda, ECS, and EC2-based apps.
• Use CodeBuild, CodeDeploy for build and release automation.
• Manage IAM roles and permissions for DevOps tools.
• Integrate pipelines with GitHub or CodeCommit.

17. ChatOps (Slack / Microsoft Teams)
• Integrate CI/CD pipeline notifications into Slack/Teams.
• Use bots to trigger deployments or query status.
• Alert incidents and metrics to ChatOps channels.
• Implement approval workflows using chat commands.
• Trigger incident response playbooks from chat.

• Build and maintain CI/CD templates for repeatable use.
• Develop and maintain internal DevOps tools or dashboards
• Participate in incident response and root cause analysis (RCA).
• Work with InfoSec on vulnerability assessments.
• Perform knowledge transfers and DevOps enablement training.

Python Training Syllabus specifically tailored for AWS, Linux, and DevOps
Engineers. This course is hands-on, project-driven, and aligns with real-world
automation, cloud infrastructure, and DevOps pipelines.

Python for AWS, Linux & DevOps Engineers – 60 Hours Practical Training

Objective:
To equip Linux system admins, cloud engineers, and DevOps professionals with
Python scripting skills to automate infrastructure, cloud services (AWS), DevOps
workflows (Jenkins, Docker, Kubernetes), and system monitoring.

Module 1: Core Python Programming (6 Hours)
• Python installation (Linux/Windows/macOS)
• Variables, Data Types, Operators
• Control flow (if, else, loops)
• Functions and Lambda
• Modules and packages
• Exception handling
Lab:
• Create simple calculator & log parser script
• Automate system update notifier

Module 2: File, Log, and Config Handling (6 Hours)
• File I/O operations
• Working with .log, .ini, .conf, .yaml, .json files
• Log rotation script
• Config validation script
Lab:
• Parse and extract errors from /var/log/messages
• Modify JSON-based AWS policy templates

Module 3: Linux System Automation with Python (8 Hours)
• Automating user creation, groups, disk monitoring
• Process monitoring & systemd integration
• os, shutil, subprocess, platform modules
• Automate crontab entries
Lab:
• Write a disk threshold alert script
• Bulk user creation using CSV and Python

Module 4: Python for AWS Cloud Automation (Boto3) (10 Hours)
• Boto3 setup & authentication
• EC2: Launch, start/stop, tag, delete
• S3: Upload, download, lifecycle, public access block
• IAM: User, role, policy management
• CloudWatch: Logs, metrics, alarms
• Lambda and SNS integration (intro level)
Lab:
• Write a script to create EC2 instances with tags
• Backup files to S3 using Python + Boto3
• Auto-delete S3 objects older than 30 days

Module 5: DevOps Tools Automation with Python (10 Hours)
• Git operations using GitPython
• Jenkins API integration (trigger builds, get status)
• Docker automation via Docker SDK for Python
• Kubernetes automation using kubernetes client
• Parsing and updating Ansible/K8s YAML files
Lab:
• Python script to build Docker image & push to Docker Hub
• Deploy Kubernetes pods using Python script
• Trigger Jenkins job and fetch console logs

Module 6: REST API Integration + JSON/XML Handling (6 Hours)
• Using requests to call external/internal APIs
• Auth (token, basic)
• GET/POST/PUT/DELETE
• Work with JSON responses and headers
Lab:
• Get AWS billing data from Cost Explorer API
• Consume a public API (weather, IP lookup)

Module 7: CLI & Tool Development (4 Hours)
• Command-line arguments using argparse and click
• Logging and error handling
• Environment detection
• Custom utilities and small Python tools
Lab:
• Create a CLI tool to manage EC2 + S3 from terminal
• Write a CLI tool to deploy apps to Docker

Module 8: Projects & Capstone Labs (10 Hours)
Project 1: AWS Infra Provisioner Tool
• Launch EC2, assign tags, attach volume, add to ELB
Project 2: Linux Audit Script
• Inventory report (disk, memory, packages, users)
Project 3: CI/CD Automation Script
• Trigger Jenkins job, build Docker image, deploy to K8s
Project 4: S3 Lifecycle Manager
• Automatically clean logs older than 30 days from S3

Tools & Libraries Used:
• Boto3 – AWS automation
• Paramiko – SSH automation
• Requests – API calls
• GitPython – Git integration
• Docker SDK – Docker control
• Kubernetes Python Client – K8s API
• pyYAML/json – Configuration file parsing
• Click/Argparse – Command-line tool creation

Deliverables:
• 60+ hours of training
• 15+ real-time automation scripts
• 4 major projects with documentation
• Resume & interview guidance
• GitHub-ready portfolio

• Docker & Kubernetes training syllabus aligned with the Certified Kubernetes Administrator (CKA) exam by the Cloud Native Computing Foundation
(CNCF). This training is designed to cover all official CKA domains with deep
hands-on labs using Docker, containerd, and Kubernetes.

• Docker & Kubernetes Training Syllabus (CKA Aligned – 70+ Hours)
• Audience: System Admins, DevOps Engineers, Cloud Admins, Developers
Format: Practical labs + Certification prep + Projects
Outcome: Prepare for CKA exam + Master real-time Kubernetes use cases

• Module 1: Introduction to Containers & Docker
• Containers vs VMs
• Docker architecture
• Installing Docker CE
• Module 2: Docker CLI & Image Operations
• Docker run, exec, ps, logs, stop, rm
• Docker images, build, tag, push
• Docker volumes, bind mounts
• Docker networks
• Module 3: Dockerfile & Containerization
• Writing Dockerfiles
• Layering, caching, ENTRYPOINT vs CMD
• Multi-stage builds
• Pushing images to Docker Hub/Harbor
• Module 4: Docker Compose
• docker-compose.yml structure
• Managing multi-container apps
• Volumes & environment variables
• Compose up/down/restart logs
• Assignments:
• Containerize a Node.js/Java/Python app
• Dockerize MySQL with persistent volume
• Docker Compose: Nginx + App + DB

Module 5: Kubernetes Core Concepts (CKA Domain 1)
• Kubernetes architecture: master & worker nodes
• kubelet, kube-apiserver, etcd, controller-manager, scheduler
• Pods, ReplicaSets, Deployments
• Labels, Selectors, Namespaces
• Labs:
• Deploy pods and manage replicas
• Label-based pod selection
• Create multiple namespaces and isolate resources

• Module 6: Configuration (CKA Domain 2)
• ConfigMaps and Secrets
• Environment variables from ConfigMaps
• Mounting ConfigMaps/Secrets as volumes
• Resource limits (CPU/memory)
• Labs:
• Inject secrets into pods securely
• Control pod resource usage with limits

• Module 7: Pod Scheduling (CKA Domain 3)
• Manual pod scheduling
• Taints and tolerations
• NodeSelector, affinity & anti-affinity
• DaemonSets, static pods
• Labs:
• Schedule pods on specific nodes using labels
• Configure DaemonSets for node monitoring

• Module 8: Networking (CKA Domain 4)
• Kubernetes networking model
• Services: ClusterIP, NodePort, LoadBalancer
• DNS, headless services
• NetworkPolicies (calico/weave)
• Ingress Controllers (NGINX)
• Labs:
• Expose app using Service and Ingress
• Apply NetworkPolicy to restrict traffic

• Module 9: Storage (CKA Domain 5)
• Volumes, emptyDir, hostPath
• PersistentVolumes and PersistentVolumeClaims
• Storage Classes and dynamic provisioning
• Labs:
• Attach persistent volume to a MySQL pod
• Test ReadWriteMany (RWX) storage with NFS

• Module 10: Logging & Monitoring (CKA Domain)
• Logging architecture
• kubectl logs, events, and describe
• Monitoring with Prometheus & Grafana
• Metrics Server
Labs:
• Setup metrics-server for HPA
• View pod logs, restart crashloop containers

• Module 11: Cluster Maintenance (CKA Domain 7)
• OS-level patching
• Certificate management (kubeadm)
• Backup & restore etcd
• Upgrade Kubernetes cluster
Labs:
• Backup and restore etcd manually
• Rotate Kubernetes certificates

Module 12: Security (CKA Domain 8)
• Role-Based Access Control (RBAC)
• ServiceAccounts and Security Context
• Pod Security Policies (PSP)
• Network Policies revisited
Labs:
• Create roles and rolebindings
• Limit pod access using securityContext

Module 13: Troubleshooting (CKA Domain 9)
• Debugging pod startup issues
• Network troubleshooting (DNS, connectivity)
• Node failures
• Resource bottlenecks
Labs:
• Analyze CrashLoopBackOff errors
• Use kubectl exec and logs for RCA

• CKA exam overview and strategy
• Killer.sh and CNCF exam simulator walkthrough
• 20 exam-style questions & scenarios
• Time-saving CLI aliases and kubectl tricks

Real-Time Projects (Hands-On Labs)
Project 1: Production-Grade App Deployment
• Dockerize app + Deploy using K8s + Expose via Ingress
• Use secrets, HPA, and persistent storage
Project 2: CI/CD on Kubernetes
• Deploy Jenkins in Kubernetes
• Auto-deploy app using Jenkins + GitHub + Docker
Project 3: Secure & Scalable Kubernetes Cluster
• Implement RBAC, NetworkPolicy, TLS
• Setup monitoring and alerting with Prometheus/Grafana

Tools & Technologies Covered:
• Docker, Docker Compose, containerd
• Kubernetes (kubeadm/kubectl/kubectl-debug)
• Helm (Intro level)
• Prometheus, Grafana, Metrics Server
• NGINX Ingress Controller
• Calico/Weave Networking

Deliverables:
• 70+ hours instructor-led/practical training
• 3 real-time projects
• CKA-aligned assignments for each module
• Resume & interview prep
• CKA practice exam simulator support

Job-Oriented Terraform Training Syllabus designed for Cloud, DevOps, and Infrastructure Engineers. It’s hands-on and aligned with real-time scenarios, multi cloud provisioning, and team-based collaboration, covering from basics to advanced infrastructure automation with projects and DevOps integrations.

Terraform Job-Oriented Training Syllabus (40–50 Hours)

Mode: Practical + Assignments + Real-Time Projects

Audience: DevOps Engineers, Cloud Admins, SREs, Infra Engineers

Tools: AWS, Azure, GCP, GitHub, Jenkins, Terraform Cloud, Vault, Ansible, Docker

Module 1: Introduction to IaC & Terraform (2 Hours)
• What is Infrastructure as Code (IaC)?
• Terraform vs CloudFormation vs ARM
• Terraform features & architecture
• Installing Terraform on Linux/Windows
Assignment:
• Install Terraform and deploy a test resource in AWS

Module 2: Terraform Basics & Workflow (4 Hours)
• Terraform CLI workflow: init, plan, apply, destroy
• Terraform directory structure and workspace concept
• Providers & backend overview
• Resource, variable, output, data, locals blocks
Assignment:
• Create AWS VPC, subnet, and EC2 instance using variables and outputs

Module 3: Variables, Data Sources & Outputs (4 Hours)
• Input variables: types, defaults, descriptions
• Variable files & dynamic assignment
• Data sources: usage and dependencies
• Output values and formatting
Assignment:
• Use AWS AMI as a data source and deploy an EC2 instance
• Output public IP and instance tags

Module 4: State Management & Backend (5 Hours)
• Understanding terraform.tfstate
• terraform state commands
• Remote backends: S3 + DynamoDB + GCS + Azure Blob
• State locking and file consistency
• Workspaces (multi-env: dev, qa, prod)
Assignment:
• Configure remote backend with AWS S3 + DynamoDB locking

Module 5: Terraform Functions, Loops & Conditionals (4 Hours)
• Built-in functions: lookup, join, concat, length, toset, etc.
• count, for_each, and for expressions
• Conditional expressions and dynamic blocks
Assignment:
• Deploy multiple EC2s using count and for_each with dynamic tagging

Module 6: Terraform Modules (5 Hours)
• Module structure: main.tf, variables.tf, outputs.tf
• Using public modules (Terraform Registry)
• Writing reusable custom modules
• Nested modules
Assignment:
• Create reusable VPC + EC2 + S3 module
• Deploy 3-tier architecture using modules

Module 7: Terraform Provisioners & Meta-Arguments (4 Hours)
• Local and remote-exec provisioners
• Connection blocks (SSH, WinRM)
• depends_on, lifecycle, ignore_changes
Assignment:
• Install Apache on EC2 via remote-exec
• Prevent resource destruction using lifecycle

Module 8: Terraform Cloud & Collaboration (3 Hours)
• Terraform Cloud vs Terraform CLI
• Workspaces, teams, and variables in TFC
• Remote execution and state storage
• Version control integration (GitHub/GitLab)
Assignment:
• Connect TFC to GitHub and auto-deploy on changes

Module 9: Terraform with AWS, Azure & GCP (6 Hours)
• Provision resources in AWS: EC2, S3, RDS, VPC, IAM
• Azure: Resource groups, VMs, storage, NSG
• GCP: Compute Engine, Storage Buckets, IAM
Assignment:
• Deploy identical infrastructure in AWS, Azure, and GCP

Module 10: Terraform with DevOps Tools (5 Hours)
• Jenkins integration with Terraform
• Ansible and Terraform workflow (pre/post config)
• Docker provisioning with Terraform
• Vault secrets integration
Assignment:
• Create Jenkins pipeline to deploy Terraform infrastructure
• Pull secrets from Vault to provision EC2

Module 11: Security, Validation & Testing (2 Hours)
• Input validation
• Sensitive values and secrets management
• Terraform validate, fmt, graph, taint
• Using tflint and checkov for security scans
Assignment:
• Write policies to prevent open S3 buckets
• Lint Terraform code and fix errors

Module 12: Troubleshooting, Optimization & Best Practices (2 Hours)
• Debugging and log tracing
• Handling dependency and version conflicts
• Code structuring tips
• Team workflows and GitOps
Assignment:
• Resolve broken module dependency
• Refactor spaghetti Terraform codebase

Capstone Projects (5–7 Hours)
Project 1: AWS Highly Available Web App Deployment
• VPC with public/private subnets, EC2, ALB, Auto Scaling, RDS
• Remote state backend, multiple modules, Jenkins pipeline
Project 2: Azure AKS Cluster with Terraform + Ansible
• Provision AKS using Terraform
• Configure K8s cluster using Ansible and kubectl via remote-exec
Project 3: Multi-Cloud S3/GCS Storage & Alerting
• Deploy S3/GCS with alerting, tagging, encryption, versioning

Certification & Interview Support:
• Terraform Associate (HashiCorp Certified) preparation tips
• 30+ real-world scenarios with solutions
• Mock interview questions & GitHub resume portfolio

Deliverables:
• 50+ hours instructor-led/practical training
• 3 real-world projects
• 30+ scripts, modules, and assignments
• Resume, GitHub repo, and certification prep support