When I started my journey in data engineering, I knew I needed structured guidance. The internet is filled with courses, tutorials, and certifications, but the real challenge was finding the ones that actually deliver the skills needed for real-world data engineering tasks. I Tried 10 Data Engineering Courses — Here Are My Top 5, based on my experience of testing different courses, evaluating their content, hands-on exercises, and real-world applicability.
After testing them, I narrowed it down to my top 5. These are the ones that genuinely stood out—the ones that provide practical skills, strong foundational knowledge, and a solid roadmap for aspiring data engineers. If you’re on the same journey, this list will help you save time and choose wisely!
So, without any further ado, let’s get started-
I Tried 10 Data Engineering Courses — Here Are My Top 5
My Selection Process
Choosing these top 5 wasn’t easy. I evaluated the courses based on:
- Content Quality – How well the concepts are explained and structured.
- Hands-on Experience – Whether the course includes practical exercises, projects, or real-world applications.
- Industry Relevance – Does it align with current data engineering job requirements?
- Instructor Clarity – Are the instructors engaging and easy to follow?
- Value for Money – Is the course worth the investment in terms of skills gained?
Beyond these criteria, I also considered whether the courses prepare you for industry-standard tools and workflows. Now, let’s see my top picks!
1. DataCamp: Associate Data Engineer in SQL
Time to Complete: 30 hours
Best For: Beginners & Intermediate learners who want a solid SQL foundation for data engineering
This course is my top pick because SQL is the backbone of data engineering. If you don’t understand how to manage, manipulate, and extract data efficiently, everything else becomes harder.
Why I Love It:
- Comprehensive SQL Coverage – Covers everything from basics to advanced SQL techniques.
- Database Design & Warehousing – Teaches star and snowflake schemas, normalization, and working with PostgreSQL and Snowflake.
- Hands-on Projects – Includes real-world SQL projects like analyzing student mental health data.
- No Prerequisites – Perfect for absolute beginners!
What You’ll Learn:
- Extract-Transform-Load (ETL) & Extract-Load-Transform (ELT) concepts
- SQL querying: joins, subqueries, grouping, filtering, and aggregation
- Database schema design and normalization
- Setting up PostgreSQL and working with Snowflake
If you’re new to data engineering, this course gives you a structured learning path with solid hands-on practice.
Extra Tip: Pair this with LeetCode SQL problems to sharpen your problem-solving skills!
2. DataCamp: Data Engineer in Python
Time to Complete: 40 hours
Best For: Those who want to master data engineering using Python
After mastering SQL, the next step is learning Python for data engineering. This course does an amazing job at bridging the gap between SQL-based data engineering and Python-based workflows.
Why It Stands Out:
- Focus on Python for Data Engineering – Covers data ingestion, transformation, and pipeline management.
- Cloud Computing Introduction – Gives you an understanding of cloud-based data engineering.
- ETL and Apache Airflow – Learn to build and manage data pipelines.
- Hands-on Projects – Apply what you learn in real-world scenarios.
What You’ll Learn:
- Python programming basics to advanced concepts
- Data cleaning, manipulation, and analysis
- Version control with Git
- Building data pipelines using ETL & ELT
- Introduction to Apache Airflow for scheduling workflows
If you want to become a Python-based data engineer, this course is a fantastic place to start.
Extra Tip: Try implementing an ETL pipeline using Airflow and AWS Lambda for hands-on practice.
3. DataCamp: Data Engineer Certification
Time to Complete: Self-paced (Usually a few months)
Best For: Those looking for a certification to showcase data engineering skills
Certifications can be a great way to prove your skills, and DataCamp’s Data Engineer Certification is perfect for beginners and intermediate learners.
Why I Recommend It:
- Real-world skill assessment – Covers SQL, Python, and data management.
- Affordable Certification – Only $25 per month (included in DataCamp’s Premium Membership).
- Hands-on Exam – A practical assessment that simulates real-world problems.
What’s Covered:
- SQL and Python for data engineering
- Data management and exploratory analysis
- Working with structured & unstructured data
- Real-world data engineering problems
This is a great option if you want to validate your skills with a certification that employers recognize.
Extra Tip: Build a personal project on GitHub using what you’ve learned to showcase your skills to employers.
4. Become a Data Engineer– Udacity
Time to Complete: 5 months
Best For: Those looking for hands-on, project-based learning
Udacity’s Nanodegree programs are known for their practical, real-world approach, and this one is no exception. It’s one of the most in-depth courses for learning data engineering from scratch.
What Makes It Stand Out:
- 4 detailed courses & 6 projects – Work on real-world industry projects.
- Mentorship & project feedback – Get guidance from industry experts.
- Covers advanced topics – Data modeling, Spark, Data Lakes, and Cloud Data Warehouses.
What You’ll Learn:
- Data modeling techniques
- Cloud-based data warehousing
- Working with Spark and Data Lakes
- Automating data pipelines
The downside? It’s a bit expensive. But if you’re serious about getting into data engineering, this course is worth the investment.
Extra Tip: Apply for Udacity scholarships to reduce costs!
5. Data Engineering, Big Data, and Machine Learning on GCP Specialization– Coursera
Time to Complete: 3 months (5 hours/week)
Best For: Learning data engineering on Google Cloud
This course, provided by Google Cloud, is perfect if you want to specialize in big data engineering on GCP.
Why It’s Worth Taking:
- Taught by Google Cloud experts – Get industry-level insights.
- Google Cloud-focused – Learn how to build scalable data pipelines.
- Covers Machine Learning & AI – Understand how ML integrates with big data engineering.
- Shareable Certificate – Add it to your LinkedIn profile or resume.
What You’ll Learn:
- Big Data & Machine Learning fundamentals
- Designing & building data pipelines on GCP
- Data Lakes, Warehouses, & Streaming Analytics
If you’re interested in cloud-based data engineering, this specialization is one of the best out there.
Extra Tip: Sign up for Google Cloud’s free tier to practice hands-on labs!
Final Thoughts
If you’re looking to break into data engineering, these 5 courses provide an excellent roadmap.
- Start with DataCamp’s SQL course to master databases.
- Move to Python-based data engineering with DataCamp’s Python track.
- Get certified with DataCamp’s Data Engineer Certification to prove your skills.
- If you want hands-on, project-based learning, go for Udacity.
- For cloud-focused data engineering, Coursera’s Google Cloud specialization is a solid choice.
Each of these courses brings something unique to the table, so choose based on your goals and learning style.
Happy learning!
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Thought of the Day…
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– John Wooden
Written By Aqsa Zafar
Founder of MLTUT, Machine Learning Ph.D. scholar at Dayananda Sagar University. Research on social media depression detection. Create tutorials on ML and data science for diverse applications. Passionate about sharing knowledge through website and social media.