Cloud Data Engineering — Course Roadmap

This program covers the complete data engineering lifecycle, including data ingestion, transformation, orchestration, and deployment across modern cloud platforms.

The curriculum is structured step-by-step to help you gain both theoretical understanding and hands-on experience.

Program Overview

• Duration: ~36 weeks (~9 months)
• Format: Live instructor-led classes (3 hours each) with recordings available
• Schedule: 2–3 classes per week
• Projects: Real-world projects in every major section
• Support: Community forum + office hours
• Prerequisites: Basic computer literacy (no prior experience required)

What You’ll Learn

• SQL for data querying, transformation, and optimization
• Python for data processing, automation, and ETL pipelines
• Apache Airflow for workflow orchestration
• Docker and CI/CD for deployment and automation
• Snowflake and dbt for modern data warehousing
• Apache Kafka for real-time data streaming
• AWS and Azure for cloud-based data engineering
• Modern AI-assisted development tools and workflows

Course Breakdown

Week 1 — Orientation

• Set up your development environment
• Create and optimize your GitHub profile
• Build and improve your LinkedIn profile
• Understand the complete learning roadmap

SQL (6 Weeks)

Master core and advanced SQL concepts, including:

• Querying and filtering data
• Joins and subqueries
• Window functions
• CTEs and stored procedures
• Indexing and optimization
• Database design fundamentals
• Building a full end-to-end SQL project

Python (6 Weeks)

Learn Python for data engineering workflows:

• Core Python programming
• Functions, OOP, and modules
• Data manipulation using Pandas
• File handling and automation
• APIs and JSON processing
• ETL pipeline development
• Building production-ready projects

Apache Airflow (3 Weeks)

Design and deploy production-grade workflows:

• DAG creation and scheduling
• Workflow orchestration
• Task dependencies and monitoring
• Real-world ETL pipelines
• Airflow deployment on AWS

CI/CD, Docker & Bash (3 Weeks)

Learn deployment and automation fundamentals:

• Docker containerization
• Bash scripting
• Linux fundamentals
• CI/CD pipelines
• GitHub Actions
• Automated deployment workflows

Agentic Vibe Engineering (2 Weeks)

Explore modern AI-powered development:

• AI-assisted coding tools
• Prompt engineering for developers
• Agent-based workflows
• Productivity-focused engineering practices

Snowflake + dbt (5 Weeks)

Build scalable cloud data warehouses:

• Snowflake fundamentals
• Data modeling and warehousing
• dbt transformations
• Testing and documentation
• Production-grade data pipelines
• Analytics engineering concepts

Apache Kafka (3 Weeks)

Develop real-time streaming systems:

• Kafka architecture
• Producers and consumers
• Event-driven systems
• Streaming pipeline development
• Real-time data processing projects

AWS (5 Weeks)

Work with core AWS data engineering services:

• Amazon S3
• AWS Glue
• Amazon Athena
• Amazon Redshift
• AWS Lambda
• IAM and security basics
• Terraform for infrastructure automation

Build scalable cloud-native data pipelines and workflows.

Azure (4 Weeks)

Implement enterprise-grade Azure solutions:

• Medallion architecture
• Azure Databricks
• Azure Synapse
• Microsoft Fabric
• Data Lake concepts
• End-to-end Azure pipeline implementation

Projects You’ll Build

• End-to-end SQL data systems
• Python-based ETL pipelines
• Airflow production workflows
• Dockerized applications with CI/CD
• Snowflake + dbt warehouse solutions
• Kafka streaming systems
• Full AWS cloud data engineering project