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Free Download Data Engineering with Python, SQL, Spark & Airflow
Published 8/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 27h 45m | Size: 25.27 GB
Build ETL pipelines with Python, SQL, PySpark, Airflow, Docker & Cloud through hands-on projects
What you'll learn
Build end-to-end data pipelines using Python, SQL, PostgreSQL, ETL/ELT workflows, and practical data engineering techniques.
Process and transform real-world datasets with Python, Pandas, NumPy, and PySpark, including large datasets with 1M+ records.
Design databases and data models, write advanced SQL queries, optimize performance, and create analytics-ready datasets.
Automate data pipelines using Docker and Airflow, work with cloud storage, and build a complete end-to-end data engineering project.
Requirements
No prior Data Engineering experience is required. Basic computer skills are sufficient. Basic programming or SQL knowledge is helpful but not required.
A computer capable of running Python, PostgreSQL, Docker, Airflow, and Apache Spark is recommended for completing the hands-on projects.
Description
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 withPostgreSQL, 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 useDocker and Airflow to automate and orchestrate data workflows, and how to process large datasets using PySpark.
The course culminates in anend-to-end Data Engineering capstone project, where you will ingest raw data, transform it with Python, load it into a database, perform SQL analytics, process data with Spark, generate reporting datasets, and automate the complete workflow.
By completing the practical projects and exercises, you'll develop a portfolio-oriented understanding of how different Data Engineering tools work together to create reliable and repeatable data pipelines.
What you'll work with
- Python and Pandas
- SQL and PostgreSQL
- ETL and ELT pipelines
- Apache Spark and PySpark
- Docker
- Apache Airflow
- Cloud storage concepts
- Data warehouses and dimensional modeling
- Real-world datasets and practical projects
Whether you're a student, aspiring Data Engineer, Data Analyst, software developer, or professional transitioning into Data Engineering, this course provides a structured path from fundamentals to building complete data pipelines.
Who this course is for
Beginners aspiring to become Data Engineers.
Students learning Data Engineering from scratch.
Data Analysts transitioning into Data Engineering.
Software developers expanding into data engineering.
Professionals seeking practical skills in Python, SQL, Spark, Airflow, and ETL.
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