Live Online Terraform Course for Data Engineering

Live Course Module: Terraform Course for Data Engineering

Total Duration: 40 Hours (5 Weeks)

Week 1: Introduction to Terraform & IaC (Beginner)

Sessions: 2 × 3–4 hours

• 0:00 – 0:45: Introduction to Infrastructure as Code (IaC)

  • Why IaC is important in Data Engineering

  • Terraform vs CloudFormation vs Ansible

• 0:45 – 1:30: Terraform Overview & Architecture

  • Providers, Resources, State files

  • Terraform CLI, HCL (HashiCorp Configuration Language) basics

• 1:30 – 2:15: Installing & Configuring Terraform

  • Local setup, environment variables

  • Initializing and validating Terraform projects

• 2:15 – 3:00: Terraform Workflow Basics

  • terraform init, plan, apply, destroy

  • Understanding state management

• 3:00 – 4:00: Hands-on Lab

  • Provision a simple AWS S3 bucket or GCP storage bucket

  • Explore Terraform state and CLI commands

Week 2: Terraform Configuration & Variables (Beginner → Intermediate)

Sessions: 2 × 3–4 hours

• 0:00 – 0:45: Terraform Configuration Files

  • Resource blocks, provider blocks, and modules

  • Using multiple .tf files

• 0:45 – 1:30: Variables and Outputs

  • Input variables, default values, type constraints

  • Output variables for pipeline integration

• 1:30 – 2:15: Terraform State Management

  • Local vs Remote state (S3, GCS, Terraform Cloud)

  • State locking and versioning

• 2:15 – 3:00: Terraform Data Sources & Interpolation

  • Reading data from cloud resources

  • Using outputs and variables in other resources

• 3:00 – 4:00: Hands-on Lab

  • Provision EC2/VM instances or Cloud SQL/PostgreSQL

  • Pass variables dynamically and retrieve outputs

Week 3: Terraform Modules, Provisioners & Lifecycle (Intermediate)

Sessions: 2 × 3–4 hours

• 0:00 – 0:45: Terraform Modules

  • Creating reusable modules

  • Calling modules and passing variables

• 0:45 – 1:30: Provisioners & Remote Execution

  • remote-exec, local-exec for bootstrapping data pipelines

  • Using provisioners for configuration

• 1:30 – 2:15: Resource Lifecycle & Dependencies

  • create_before_destroy, depends_on, ignoring changes

  • Managing complex interdependent resources

• 2:15 – 3:00: Terraform Workspaces

  • Multi-environment management (dev, staging, prod)

  • Best practices for workspace separation

• 3:00 – 4:00: Hands-on Lab

  • Build a multi-resource data pipeline infrastructure

  • Apply modules, provisioners, and lifecycle rules

Week 4: Advanced Terraform – CI/CD, Security & Cloud Integration (Intermediate → Advanced)

Sessions: 2 × 3–4 hours

• 0:00 – 0:45: Terraform and CI/CD

  • Integrate Terraform with Jenkins, GitHub Actions, GitLab CI/CD

  • Automated plan, apply, and destroy pipelines

• 0:45 – 1:30: Security & Best Practices

  • Manage secrets (AWS KMS, GCP Secret Manager, Vault)

  • IAM policies, roles, and least privilege principle

• 1:30 – 2:15: Remote State & Collaboration

  • Terraform Cloud / Enterprise features

  • Backend configuration for team collaboration

• 2:15 – 3:00: Terraform Debugging & Troubleshooting

  • Debug logs, state issues, and drift detection

• 3:00 – 4:00: Hands-on Lab

  • Provision a full data engineering environment (VMs, Storage, Networking)

  • Apply best practices and integrate with CI/CD

Week 5: Capstone Project – End-to-End Data Pipeline Deployment (Advanced)

Sessions: 2 × 3–4 hours

• 0:00 – 0:45: Project Overview

  • Deploy a containerized ETL pipeline on cloud using Terraform

  • Components: Kafka/Spark/PostgreSQL/MinIO

• 0:45 – 1:30: Multi-Environment Deployment

  • Implement dev/staging/prod environments using workspaces

  • Apply modules and variables for reusable infrastructure

• 1:30 – 2:15: Scaling & Monitoring Setup

  • Auto-scaling compute resources

  • Cloud monitoring (CloudWatch, Stackdriver, Azure Monitor)

• 2:15 – 3:00: Project Deployment & Testing

  • Apply Terraform scripts

  • Validate the deployed pipeline functionality

• 3:00 – 4:00: Project Review & Certification

  • Peer review, instructor feedback

  • Best practices recap and Q&A

Key Learning Outcomes

1. Understand Terraform architecture and IaC principles for Data Engineering.

2. Write and manage Terraform configurations and modules.

3. Deploy multi-environment, reusable cloud infrastructure.

4. Integrate Terraform with CI/CD pipelines for automated deployments.

5. Apply security best practices and manage state for collaborative projects.

6. Deploy and manage an end-to-end cloud-based data engineering pipeline.