Learn Data Engineering from the ground up by building practical data pipelines with Python, SQL, PySpark, Airflow, Docker, databases, and cloud storage. This hands-on Data Engineering course is designed for beginners who want to understand how modern data pipelines are designed, built, automated, and used in real-world environments.
You will start with the fundamentals of Data Engineering, including the data lifecycle, structured and unstructured data, batch versus streaming processing, and the modern data stack. From there, you’ll build a strong foundation in relational databases, data modeling, SQL, and Python for data processing. You will work with PostgreSQL, Python, Pandas, NumPy, SQL, Apache Spark, PySpark, Docker, and Apache Airflow, while learning how these technologies fit together in practical data engineering workflows.
The course goes beyond theory. You will perform hands-on exercises involving database creation, schema design, SQL analytics, data cleaning, ETL pipelines, CSV-to-database loading, data quality validation, pipeline automation, Spark-based processing, cloud storage, and data warehousing concepts. You will also learn how to use Docker and Airflow to automate and orchestrate data workflows, and how to process large datasets using PySpark.
