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
