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⚙️ DevOps Engineer Learning Path

Comparative positioning note

This document is written from the perspective of Microsoft Azure, Cloud Scale Analytics, and CSA Loom. Any description of third-party or competing products, services, pricing, or capabilities is derived from publicly available documentation and sources believed accurate at the time of writing, and is provided for general comparison only. We do not claim expertise in, or authority over, any non-Microsoft product or service; the respective vendor's official documentation is the authoritative source for their offerings, which may change over time. Nothing here is intended to disparage any vendor — where a competing product has genuine advantages, we aim to note them honestly. Verify all third-party details against the vendor's current official documentation before making decisions.

Status Duration Level

Master DevOps practices for Azure analytics platforms. Build CI/CD pipelines, implement Infrastructure as Code, and automate deployment, monitoring, and operations for data engineering workloads.

🎯 Learning Objectives

After completing this learning path, you will be able to:

  • Implement CI/CD pipelines for data engineering workflows
  • Automate infrastructure deployment with Infrastructure as Code (IaC)
  • Deploy Azure analytics services using Bicep, Terraform, and ARM templates
  • Configure monitoring, logging, and alerting for data platforms
  • Implement disaster recovery and business continuity strategies
  • Optimize costs and resource utilization
  • Ensure security, compliance, and governance automation

📋 Prerequisites Checklist

Before starting this learning path, ensure you have:

Required Knowledge

  • DevOps fundamentals - Understanding of CI/CD, version control, automation
  • Azure basics - Familiarity with Azure portal, resources, and services
  • Scripting - Proficiency in PowerShell, Bash, or Python
  • Git - Experience with branching, merging, pull requests
  • Linux and Windows - Basic system administration skills

Required Skills

  • Command line - Comfortable with terminal/PowerShell
  • YAML/JSON - Understanding of configuration file formats
  • Networking - Basic TCP/IP, DNS, firewalls, VPN concepts
  • Security - Understanding of authentication, authorization, encryption

Required Access

  • Azure subscription with Owner or Contributor role
  • GitHub or Azure DevOps account for version control and CI/CD
  • Development tools - VS Code, Azure CLI, PowerShell, Git
  • Sufficient credits (~$250-300 for complete path)
  • Experience with containers (Docker)
  • Familiarity with Kubernetes concepts
  • Understanding of data engineering workflows
  • Exposure to monitoring and logging tools

🗺️ Learning Path Structure

This path consists of 4 progressive phases from automation basics to advanced DevOps practices:

graph LR
    A[Phase 1:<br/>Foundation] --> B[Phase 2:<br/>IaC & CI/CD]
    B --> C[Phase 3:<br/>Operations]
    C --> D[Phase 4:<br/>Advanced]

    style A fill:#90EE90
    style B fill:#87CEEB
    style C fill:#FFA500
    style D fill:#FF6B6B

Time Investment

  • Full-Time (40 hrs/week): 10-12 weeks
  • Part-Time (20 hrs/week): 16-20 weeks
  • Casual (10 hrs/week): 24-30 weeks

📚 Phase 1: DevOps Foundation (2-3 weeks)

Goal: Build foundational automation and scripting skills for Azure

Module 1.1: Azure DevOps Fundamentals (10 hours)

Learning Objectives:

  • Understand DevOps principles and practices
  • Navigate Azure Portal, Azure CLI, and PowerShell
  • Manage Azure resources programmatically
  • Implement proper authentication and authorization

Hands-on Exercises:

  1. Lab 1.1.1: Set up Azure CLI and PowerShell environment
  2. Lab 1.1.2: Create and manage Azure resources via CLI
  3. Lab 1.1.3: Implement service principal authentication
  4. Lab 1.1.4: Manage Azure RBAC with scripts

Resources:

Assessment:

  • Automate creation of Azure Synapse workspace using CLI
  • Implement RBAC assignment script

Module 1.2: Scripting and Automation (12 hours)

Learning Objectives:

  • Write PowerShell scripts for Azure automation
  • Use Python for Azure resource management
  • Implement error handling and logging
  • Create reusable automation modules

Hands-on Exercises:

  1. Lab 1.2.1: Build PowerShell module for resource deployment
  2. Lab 1.2.2: Create Python scripts using Azure SDK
  3. Lab 1.2.3: Implement logging and error handling
  4. Lab 1.2.4: Schedule automation with Azure Automation

Sample Scripts:

  • Resource health check automation
  • Cost reporting and alerts
  • Backup and recovery automation
  • Security compliance scanning

Module 1.3: Version Control and Collaboration (8 hours)

Learning Objectives:

  • Master Git workflows (branching, merging, rebasing)
  • Implement GitFlow or trunk-based development
  • Use pull requests for code review
  • Manage secrets and sensitive data

Hands-on Exercises:

  1. Lab 1.3.1: Set up Git repository with proper structure
  2. Lab 1.3.2: Implement branching strategy
  3. Lab 1.3.3: Create pull request workflow
  4. Lab 1.3.4: Manage secrets with Azure Key Vault

Resources:

📚 Phase 2: Infrastructure as Code & CI/CD (3-4 weeks)

Goal: Master IaC and build automated deployment pipelines

Module 2.1: Infrastructure as Code with Bicep (14 hours)

Learning Objectives:

  • Understand ARM template structure and syntax
  • Write Bicep templates for Azure resources
  • Implement modular and reusable templates
  • Manage template parameters and outputs

Hands-on Exercises:

  1. Lab 2.1.1: Create Bicep templates for Synapse workspace
  2. Lab 2.1.2: Build modular templates with parameters
  3. Lab 2.1.3: Implement template validation and testing
  4. Lab 2.1.4: Deploy multi-resource environments

Sample Templates:

// Azure Synapse Workspace
resource synapseWorkspace 'Microsoft.Synapse/workspaces@2021-06-01' = {
  name: workspaceName
  location: location
  identity: {
    type: 'SystemAssigned'
  }
  properties: {
    defaultDataLakeStorage: {
      accountUrl: storageAccountUrl
      filesystem: fileSystemName
    }
    sqlAdministratorLogin: sqlAdminUsername
    sqlAdministratorLoginPassword: sqlAdminPassword
  }
}

Module 2.2: Infrastructure as Code with Terraform (14 hours)

Learning Objectives:

  • Understand Terraform workflow (init, plan, apply)
  • Write Terraform configurations for Azure
  • Manage state and backends
  • Implement modules and workspaces

Hands-on Exercises:

  1. Lab 2.2.1: Set up Terraform with Azure provider
  2. Lab 2.2.2: Create reusable Terraform modules
  3. Lab 2.2.3: Manage remote state with Azure Storage
  4. Lab 2.2.4: Implement multi-environment deployments

Sample Configuration:

resource "azurerm_synapse_workspace" "main" {
  name                                 = var.workspace_name
  resource_group_name                  = azurerm_resource_group.main.name
  location                             = azurerm_resource_group.main.location
  storage_data_lake_gen2_filesystem_id = azurerm_storage_data_lake_gen2_filesystem.main.id
  sql_administrator_login              = var.sql_admin_username
  sql_administrator_login_password     = var.sql_admin_password

  identity {
    type = "SystemAssigned"
  }
}

Module 2.3: CI/CD Pipelines (16 hours)

Learning Objectives:

  • Build CI/CD pipelines with GitHub Actions
  • Implement Azure DevOps pipelines
  • Automate testing and validation
  • Deploy across multiple environments

Hands-on Exercises:

  1. Lab 2.3.1: Create GitHub Actions workflow for IaC deployment
  2. Lab 2.3.2: Build Azure Pipelines for data engineering
  3. Lab 2.3.3: Implement automated testing in pipelines
  4. Lab 2.3.4: Deploy to dev, staging, production environments

Resources:

Sample GitHub Actions Workflow:

name: Deploy Synapse Infrastructure

on:
  push:
    branches: [main]
  pull_request:
    branches: [main]

jobs:
  deploy:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3

      - name: Azure Login
        uses: azure/login@v1
        with:
          creds: ${{ secrets.AZURE_CREDENTIALS }}

      - name: Deploy Bicep
        run: |
          az deployment group create \
            --resource-group ${{ secrets.RESOURCE_GROUP }} \
            --template-file ./infrastructure/main.bicep \
            --parameters ./infrastructure/parameters.json

📚 Phase 3: Operations and Monitoring (2-3 weeks)

Goal: Implement comprehensive monitoring, logging, and operational excellence

Module 3.1: Monitoring and Observability (12 hours)

Learning Objectives:

  • Configure Azure Monitor for data workloads
  • Set up Log Analytics workspaces
  • Create custom dashboards and workbooks
  • Implement distributed tracing

Hands-on Exercises:

  1. Lab 3.1.1: Set up Azure Monitor for Synapse Analytics
  2. Lab 3.1.2: Create Log Analytics queries (KQL)
  3. Lab 3.1.3: Build monitoring dashboards
  4. Lab 3.1.4: Implement Application Insights for pipelines

Resources:

Module 3.2: Alerting and Incident Response (10 hours)

Learning Objectives:

  • Create metric and log-based alerts
  • Implement action groups and notifications
  • Design runbooks for common incidents
  • Integrate with incident management systems

Hands-on Exercises:

  1. Lab 3.2.1: Set up alerts for critical metrics
  2. Lab 3.2.2: Create automated remediation with Azure Automation
  3. Lab 3.2.3: Build incident response runbooks
  4. Lab 3.2.4: Integrate with PagerDuty/ServiceNow

Alert Examples:

  • Pipeline failure notifications
  • Resource utilization thresholds
  • Cost anomaly detection
  • Security compliance violations

Module 3.3: Backup, Recovery, and Business Continuity (12 hours)

Learning Objectives:

  • Implement backup strategies for data platforms
  • Design disaster recovery procedures
  • Test failover and recovery scenarios
  • Document RTO and RPO requirements

Hands-on Exercises:

  1. Lab 3.3.1: Configure Azure Backup for data services
  2. Lab 3.3.2: Implement geo-replication for critical data
  3. Lab 3.3.3: Create and test disaster recovery plan
  4. Lab 3.3.4: Automate backup validation

Resources:

📚 Phase 4: Advanced DevOps Practices (3-4 weeks)

Goal: Master advanced automation, security, and optimization

Module 4.1: GitOps and Advanced Automation (14 hours)

Learning Objectives:

  • Implement GitOps principles for infrastructure
  • Use ArgoCD or Flux for continuous deployment
  • Automate configuration management
  • Implement policy as code

Hands-on Exercises:

  1. Lab 4.1.1: Set up GitOps workflow for infrastructure
  2. Lab 4.1.2: Implement Azure Policy as code
  3. Lab 4.1.3: Automate configuration drift detection
  4. Lab 4.1.4: Build self-healing infrastructure

Module 4.2: Security Automation (12 hours)

Learning Objectives:

  • Implement DevSecOps practices
  • Automate security scanning and compliance
  • Manage secrets and certificates
  • Implement network security automation

Hands-on Exercises:

  1. Lab 4.2.1: Integrate security scanning in CI/CD
  2. Lab 4.2.2: Automate vulnerability management
  3. Lab 4.2.3: Implement certificate rotation automation
  4. Lab 4.2.4: Set up Azure Security Center policies

Resources:

Module 4.3: Cost Optimization and FinOps (10 hours)

Learning Objectives:

  • Implement cost monitoring and alerting
  • Automate resource rightsizing
  • Implement scheduled scaling
  • Create cost allocation and chargeback reports

Hands-on Exercises:

  1. Lab 4.3.1: Build cost monitoring dashboards
  2. Lab 4.3.2: Implement auto-scaling for Spark pools
  3. Lab 4.3.3: Create cost optimization recommendations
  4. Lab 4.3.4: Automate resource cleanup

Automation Examples:

  • Auto-pause idle Synapse pools
  • Schedule compute for business hours
  • Cleanup orphaned resources
  • Implement budget alerts

Module 4.4: Capstone Project (20 hours)

Requirements:

Build a complete DevOps solution including:

  1. Infrastructure as Code: Multi-environment deployment (dev, staging, prod)
  2. CI/CD Pipelines: Automated testing, deployment, and rollback
  3. Monitoring: Comprehensive observability with dashboards and alerts
  4. Security: Automated security scanning and compliance
  5. Documentation: Runbooks, architecture diagrams, operational guides
  6. Disaster Recovery: Tested backup and recovery procedures

Deliverables:

  • Complete IaC templates (Bicep or Terraform)
  • CI/CD pipeline configurations
  • Monitoring and alerting setup
  • Security and compliance automation
  • Operational runbooks and documentation
  • Cost optimization recommendations

🎓 Certification Alignment

This learning path prepares you for:

  • Azure DevOps Engineer Expert (AZ-400) - Primary focus
  • Azure Administrator Associate (AZ-104) - Foundational
  • Azure Data Engineer Associate (DP-203) - Data platform focus

📊 Skills Assessment

Self-Assessment Checklist

Rate your skills (1-5, where 5 is expert):

Infrastructure as Code (Target: 4-5)

  • Write and maintain Bicep/Terraform templates
  • Implement modular and reusable infrastructure code
  • Manage state and handle conflicts
  • Deploy multi-environment infrastructure

CI/CD (Target: 4-5)

  • Build and maintain CI/CD pipelines
  • Implement automated testing
  • Manage pipeline secrets and variables
  • Deploy across multiple environments

Operations (Target: 4-5)

  • Configure comprehensive monitoring
  • Create and manage alerts
  • Write operational runbooks
  • Implement backup and recovery

Automation (Target: 3-4)

  • Write PowerShell and Python automation
  • Implement scheduled tasks
  • Create self-healing systems
  • Automate security and compliance

💡 Learning Tips

Study Strategies

  • Automate everything: Look for repetitive tasks to automate
  • Test thoroughly: Always validate IaC and pipelines in dev environment
  • Document as you go: Create runbooks and wiki pages
  • Learn from failures: Analyze pipeline failures to improve
  • Stay current: Follow Azure DevOps updates and best practices

Books

  • "The DevOps Handbook" by Gene Kim
  • "Infrastructure as Code" by Kief Morris
  • "Continuous Delivery" by Jez Humble
  • "Site Reliability Engineering" by Google

Communities

  • Azure DevOps Community
  • r/azuredevops subreddit
  • Azure DevOps Discord/Slack channels
  • Local DevOps meetups

Practice Projects

  • Automate your development environment setup
  • Build IaC templates for common architectures
  • Create reusable pipeline templates
  • Contribute to open-source DevOps projects

🔗 Next Steps

After completing this path:

  • Specialize: Focus on Kubernetes, containers, or cloud-native
  • Expand: Learn multi-cloud DevOps (AWS, GCP)
  • Lead: Drive DevOps transformation in your organization
  • Share: Present at meetups and conferences

Advanced Topics

  • Kubernetes and containerization
  • Service mesh (Istio, Linkerd)
  • Chaos engineering
  • Advanced GitOps patterns
  • Platform engineering

🎉 Success Stories

"This learning path transformed how we deploy analytics infrastructure. We went from manual deployments taking days to fully automated deployments in minutes." - David, DevOps Engineer

"The CI/CD and monitoring modules were game-changers. We now have full visibility into our data platform and can respond to issues proactively." - Sarah, Platform Engineer

📞 Getting Help

Ready to start? Begin with Phase 1: DevOps Foundation

Last Updated: January 2025 Learning Path Version: 1.0 Maintained by: DevOps Team