Data Engineering Project: SQL, Python, Airflow, Docker, CI/CD
Udemy · Matthew Schembri · Updated
AI Tutor Rating
8.2/10
Duration
5 hours video
Classes
66
Become a Data Engineer by learning APIs, SQL, Python, Docker, Airflow, CI/CD, Functional/Data Quality Tests and more.
The 'Data Engineering Project: SQL, Python, Airflow, Docker, CI/CD' course on Udemy is a 5-hour, 66-lecture project-based program designed for learners aiming to build practical data engineering skills. It focuses on orchestrating workflows with Airflow and Docker, building end-to-end data pipelines with CI/CD, and implementing data quality testing. The curriculum moves from fundamentals through Python practice, data quality testing, applied Docker, building with Airflow, SQL techniques, and advanced topics. This course serves individuals with basic Python and SQL knowledge who want to apply these tools in a cohesive, project-focused data engineering context.
What you'll learn in Data Engineering Project: SQL, Python, Airflow, Docker, CI/CD
Our Review of Data Engineering Project: SQL, Python, Airflow, Docker, CI/CD
This course adopts a project-focused structure, integrating its listed technologies into a single, applied workflow. The 5-hour video format across 66 lectures suggests a dense, fast-paced curriculum that prioritizes practical demonstration over foundational theory. The learning outcomes and curriculum chapters indicate a learner will finish with hands-on experience orchestrating a pipeline using Airflow and Docker, implementing CI/CD practices for data workflows, and writing functional and data quality tests. This is a skills-application course, not a deep-dive into any single tool's theory.
The teaching format is video-only, which suits visual learners following along with code and configuration. The prerequisite of basic Python and SQL is critical; the course dives directly into applying these skills within a data engineering stack. At its promotional price of $12.99, the course offers significant value for the volume of integrated tooling covered, and the included certificate provides a tangible completion milestone. However, the value is contingent on a learner's ability to keep pace with the applied, multi-tool project build without extensive supplemental explanation.
Pros and cons of Data Engineering Project: SQL, Python, Airflow, Docker, CI/CD
Pros
- Project-based curriculum integrates multiple key data engineering tools (SQL, Python, Airflow, Docker, CI/CD) into a single workflow.
- Clear, applied learning outcomes focused on building and orchestrating a complete data pipeline.
- Covers modern, in-demand practices like data quality testing and CI/CD for data workflows.
- Strong value proposition at its current price point for the breadth of technologies covered.
- Includes a certificate of completion, adding formal recognition to the project work.
Things to consider
- Requires solid prerequisite knowledge of basic Python and SQL to be successful.
- Video-only format may not suit learners who prefer text-based or interactive coding exercises.
- The 5-hour duration for covering this many tools suggests a fast pace that could be challenging for complete beginners.
Who should take Data Engineering Project: SQL, Python, Airflow, Docker, CI/CD?
This course is best for a learner with foundational Python and SQL skills who wants to see how these tools combine with Airflow, Docker, and CI/CD in a real project. It fits someone aiming to build a portfolio piece that demonstrates modern pipeline orchestration and testing practices, rather than seeking deep theoretical knowledge in any one component.
Course curriculum for Data Engineering Project: SQL, Python, Airflow, Docker, CI/CD
Data Engineering Project: SQL, Python, Airflow, Docker, CI/CD at a glance
| Provider | Udemy |
|---|---|
| Instructor | Matthew Schembri |
| Level | Advanced |
| Time to complete | 5 hours video |
| Pricing | $12.99 |
| Certificate | Certificate |
| Prerequisites | Basic Python and SQL |
Fit
Best for
Not ideal for
The bottom line on Data Engineering Project: SQL, Python, Airflow, Docker, CI/CD
Data Engineering Project: SQL, Python, Airflow, Docker, CI/CD delivers strong practical value for its price, successfully packaging a modern toolset into a single project workflow. It is an efficient way to gain applied, integrative experience, but learners must enter with the advertised prerequisites to fully benefit from its fast-paced, project-driven approach.
Data Engineering Project: SQL, Python, Airflow, Docker, CI/CD: frequently asked questions
What is the main focus of the Data Engineering Project course on Udemy?
The main focus is building an end-to-end data engineering project. The course teaches you to orchestrate workflows with Airflow and Docker, build data pipelines with CI/CD, and implement data quality testing, integrating SQL, Python, and other modern tools.
What level of prior knowledge do I need before taking this data engineering course?
You need basic Python and SQL knowledge. The course builds directly on these prerequisites to teach applied data engineering with Airflow, Docker, and CI/CD, so comfort with foundational coding and queries is essential.
Does the Udemy Data Engineering Project course offer a certificate and is it worth the cost?
Yes, the course offers a certificate of completion. At its listed price of $12.99, it provides strong value for learners seeking a project-based introduction to a integrated modern data stack, with the certificate serving as proof of skill application.
How does this project-based course compare to a typical introductory tutorial for each tool?
Unlike separate tutorials, this course teaches how to connect tools like Airflow, Docker, SQL, Python, and CI/CD into a single pipeline. The value is in the integration and project context, showing how the technologies work together in a real-world workflow.
How can I get the most out of the Data Engineering Project course by Matthew Schembri?
To get the most out of it, ensure you meet the basic Python and SQL prerequisites. Actively code along with the 66 video lectures, and treat the completed project as a portfolio piece that demonstrates your ability to build and orchestrate a modern data pipeline.