Data Engineering Project SQL, Python, Airflow, Docker, CI/CD | Udemy [Update 08/2025]
English | Size: 2 GB
Genre: eLearning[/center]

Become a Data Engineer by Learning APIs, SQL, Python, Docker, Airflow, CI/CD, Functional & Data Quality Tests & More!

What you'll learn
Build Python scripts for data extraction by interacting with APIs using Postman, loading into the data warehouse and transforming (ELT)
Use PostgreSQL as a data warehouse. Interact with the data warehouse using both psql & DBeaver
Discover how to containerize data applications using Docker, making your data pipelines portable and easy to scale.
Master the basics of orchestrating and automating your data workflows with Apache Airflow, a must-have tool in data engineering.
Understand how to perform unit, integration & end-to-end (E2E) tests using a combination of pytest and Airflow's DAG tests to validate your data pipelines.
Implement data quality tests using SODA to ensure your data meets business and technical requirements.
Learn to automate deployment pipelines using GitHub Actions to ensure smooth, continuous integration and delivery.

Data Engineering is the backbone of modern data-driven companies. To excel, you need experience with the tools and processes that power data pipelines in real-world environments. This course gives you practical, project-based learning with the following tools PostgreSQL, Python, Docker, Airflow, Postman, SODA and Github Actions. I will guide you as to how you can use these tools.

What you will learn in the course:

Python for Data Engineering: Build Python scripts for data extraction by interacting with APIs using Postman, loading into the data warehouse and transforming (ELT)

SQL for Data Pipelines: Use PostgreSQL as a data warehouse. Interact with the data warehouse using both psql & DBeaver

Docker for Containerized Deployments: Discover how to containerize data applications using Docker, making your data pipelines portable and easy to scale.

Airflow for Workflow Automation: Master the basics of orchestrating and automating your data workflows with Apache Airflow, a must-have tool in data engineering.

Testing and Data Quality Assurance: Understand how to perform unit, integration & end-to-end (E2E) tests using a combination of pytest and Airflow's DAG tests to validate your data pipelines. Implement data quality tests using SODA to ensure your data meets business and technical requirements.

CI/CD for Automated Testing & Deployment: Learn to automate deployment pipelines using GitHub Actions to ensure smooth, continuous integration and delivery.

Who this course is for:
Aspiring Data Engineers: If you're just starting out and want to learn Data Engineering by working with real tools and projects, this course will provide you with the foundational skills you need to start your career.
Beginner Data Professionals: If you have some experience as a Data Engineer/ Data Scientist but want to deepen your understanding of essential tools like Docker, CI/CD, and automated testing, this course will help you build on what you already know.
Data Enthusiasts: Those passionate about data and interested in getting practical, hands-on experience with the tools used by modern Data Engineers.

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