README

Data Engineering Zoomcamp: A Free 9-Week Course on Data Engineering Fundamentals



Master the fundamentals of data engineering by building an end-to-end data pipeline from scratch. Gain hands-on experience with industry-standard tools and best practices.







Join Slack •
course-data-engineering Channel •
Telegram Announcements •
Course Playlist •
FAQ







 Quick Links

| Resource | Link |
|----------|------|
| Course materials | GitHub repository |
| Video lectures | YouTube playlist |
| Documentation | Zoomcamp Logistics · Data Engineering Zoomcamp |
| Course platform (deadlines, homework) | courses.datatalks.club |
| Slack channel | course-data-engineering |
| Announcements | Telegram |
| FAQ | FAQ document |

 About the Course

This free 9-week course teaches the fundamentals of data engineering by building an end-to-end data pipeline from scratch. It consists of structured modules, hands-on workshops, and a final project, giving you practical experience with industry-standard tools and best practices.

 Who Should Join

This course is for developers, analysts, and data scientists who want to learn how to build data pipelines and work with the modern data engineering stack. No prior data engineering experience is necessary.

 Prerequisites

To get the most out of this course, you should have:

- Basic coding experience
- Familiarity with SQL
- Experience with Python (helpful but not required)

No prior data engineering experience is necessary.

 How to Take the Course

There are two ways to follow the course: live and self-paced.

| | Live Cohort | Self-Paced |
|-|-|-|
| Start | January 2027 | Anytime |
| Lectures | Pre-recorded | Pre-recorded |
| Homework | Graded | Available but not scored |
| Leaderboard | ✅ Yes | ❌ No |
| Peer Review | ✅ Yes | ❌ No |
| Certificate | ✅ Yes | ❌ No |
| Cost | Free | Free |
| Register | Sign up here | Just start learning! |

 [!IMPORTANT]
 "Live cohort" does not mean live classes. All lectures are pre-recorded. "Live" means working alongside others with deadlines, scored homework, a leaderboard, peer review, and a certificate at the end.

Self-paced steps:

1. Follow the materials on GitHub
2. Ask questions and share progress in Slack
3. Do the homework (self-checked) and build a project for your portfolio

 Syllabus

 Module 1: Containerization and Infrastructure as Code

- Introduction to GCP
- Docker and Docker Compose
- Running PostgreSQL with Docker
- Infrastructure setup with Terraform
- Homework

 Module 2: Workflow Orchestration

- Data Lakes and Workflow Orchestration
- Workflow orchestration with Kestra
- Homework

 Workshop 1: Data Ingestion

- API reading and pipeline scalability
- Data normalization and incremental loading
- Homework

 Module 3: Data Warehousing

- Introduction to BigQuery
- Partitioning, clustering, and best practices
- Machine learning in BigQuery

 Module 4: Analytics Engineering

- Analytics Engineering and Data Modeling
- dbt (data build tool) with DuckDB & BigQuery
- T