Live Online Pulumi Course for Data Engineering

Live Course Module: Pulumi Course for Data Engineering

Total Duration: 40 Hours (5 Weeks)

Week 1: Introduction to Pulumi & Infrastructure as Code (IaC) (Beginner)

Sessions: 2 × 3–4 hours

• 0:00 – 0:45: Introduction to IaC & Pulumi

  • IaC concepts and benefits in Data Engineering

  • Pulumi vs Terraform, CloudFormation, Ansible

• 0:45 – 1:30: Pulumi Architecture & Components

  • Pulumi CLI, SDKs, Pulumi Service

  • Stacks, Projects, and Programs

• 1:30 – 2:15: Installing Pulumi & Environment Setup

  • Node.js / Python / Go setup for Pulumi

  • Authenticating with cloud providers (AWS, GCP, Azure)

• 2:15 – 3:00: Pulumi Workflow Basics

  • pulumi new, pulumi up, pulumi preview, pulumi destroy

  • Understanding state management and stacks

• 3:00 – 4:00: Hands-on Lab

  • Create first Pulumi stack

  • Provision a simple cloud resource (S3 bucket / GCS bucket)

Week 2: Pulumi Programming Model & Resource Management (Beginner → Intermediate)

Sessions: 2 × 3–4 hours

• 0:00 – 0:45: Pulumi Programming Model

  • Using Pulumi SDKs in Python, Node.js, or Go

  • Resource declarations, properties, and options

• 0:45 – 1:30: Pulumi Resources & Providers

  • Cloud provider resources (AWS, GCP, Azure)

  • Third-party providers for data tools (Databricks, Snowflake)

• 1:30 – 2:15: Inputs, Outputs, and Dependencies

  • Handling dynamic inputs

  • Resource dependencies and implicit ordering

• 2:15 – 3:00: Variables, Configuration & Secrets

  • Pulumi configuration files

  • Secure secret management for sensitive data

• 3:00 – 4:00: Hands-on Lab

  • Provision a multi-resource data pipeline (VMs + Storage + Database)

  • Use configuration and secrets for pipeline credentials

Week 3: Advanced Pulumi Concepts (Intermediate)

Sessions: 2 × 3–4 hours

• 0:00 – 0:45: Pulumi Components & Reusable Modules

  • Creating custom components for reusable infrastructure

  • Parameterized modules for pipelines

• 0:45 – 1:30: Automation API & CI/CD Integration

  • Pulumi Automation API for programmatic deployment

  • Integrate with Jenkins, GitHub Actions, or GitLab CI

• 1:30 – 2:15: Resource Lifecycle & Update Strategies

  • Handling updates, deletes, and replacements

  • Preventing downtime and drift detection

• 2:15 – 3:00: Multi-Stack & Environment Management

  • Development, staging, production stacks

  • Using stack references and cross-stack dependencies

• 3:00 – 4:00: Hands-on Lab

  • Build a reusable component for a Spark + Kafka + PostgreSQL pipeline

  • Deploy across multiple stacks/environments

Week 4: Monitoring, Security & Cloud Integration (Intermediate → Advanced)

Sessions: 2 × 3–4 hours

• 0:00 – 0:45: Pulumi Monitoring & Logging

  • Track resource changes and deployments

  • Integrate with Prometheus/Grafana for monitoring deployed resources

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

  • Managing secrets, IAM roles, and least privilege policies

  • Cloud provider security integrations

• 1:30 – 2:15: Advanced Cloud Resources

  • Provision serverless functions (AWS Lambda, GCP Cloud Functions)

  • Deploy managed databases, data lakes, and storage solutions

• 2:15 – 3:00: Error Handling & Debugging

  • Pulumi logs, stack outputs, and troubleshooting resource failures

• 3:00 – 4:00: Hands-on Lab

  • Deploy a secure, monitored, multi-service data pipeline

  • Integrate secrets and cloud monitoring

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

Sessions: 2 × 3–4 hours

• 0:00 – 0:45: Project Overview

  • Deploy a complete data engineering pipeline using Pulumi

  • Components: Kafka → Spark → PostgreSQL → Object Storage

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

  • Deploy across dev/staging/prod stacks

  • Manage configuration and secrets per environment

• 1:30 – 2:15: Scaling & Performance Optimization

  • Auto-scaling compute resources

  • Network optimization for data pipelines

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

  • Apply Pulumi scripts

  • Validate end-to-end pipeline functionality

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

  • Presentation, peer review, instructor feedback

  • Best practices and final Q&A

Key Learning Outcomes

1. Understand Pulumi architecture and IaC concepts for Data Engineering.

2. Write Pulumi programs using Python, Node.js, or Go for cloud infrastructure.

3. Create reusable components and manage multi-environment stacks.

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

5. Apply security best practices and monitor deployed resources.

6. Deploy a full end-to-end cloud-based data engineering pipeline.