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Complete Python, SQL & Spark for Data Engineering Course
Highest Rated
Rating: 5.0 out of 5(29 ratings)
76 students

Complete Python, SQL & Spark for Data Engineering Course

Master Python, SQL and Spark with Real World End to End Projects and Master Data Engineering from Scratch!
Highest Rated
Created byHema Sundar Thulugu
Last updated 5/2026
English

What you'll learn

  • You will master Python, SQL and Spark with Real World End to End Projects
  • You will Build end-to-end data engineering projects from scratch
  • You will be able to Confidently crack data engineering interviews
  • You will be prepared for roles such as Data Engineer, ETL Developer, and Big Data Engineer

Course content

6 sections • 71 lectures • 31h 10m total length

  • Python Introduction10:41

    Explore the six-phase master class for dbt, covering production setup and runtime, core modeling engine, testing and quality enforcement, documentation and observability, performance engineering, and environment and production strategy.

  • What is Python in Data Engineering20:00
  • Project Structuring25:47

    Initialize Airflow with Docker Compose, launching containers for scheduler, API server, and metadata database, and access the UI at localhost:8080 to manage DAGs and logs.

  • Project Structuring Part - 222:20

    Explore how Airflow DAGs define dependencies to run tasks in parallel or in sequence, and follow the dag life cycle from dag file creation to scheduling and execution.

  • Data Structures25:28

    Show how to create a basic Apache Airflow DAG with extract, transform, and load tasks, transforming an item list to uppercase and printing each loaded item.

  • Data Structures Part - 25:26
  • Raw API Response in Python26:14

    Diagnose airflow dag failures by inspecting task logs and using the error with chat GPT for fixes, then demonstrate dag creation in VS Code with start date and dag decorators.

  • CSV Read and Write in Python27:12

    Explore the roles of scheduler, executor, worker, database, and web server in a data engineering pipeline, and compare local, sequential, Kubernetes, and other executors for horizontal scaling.

  • Json in Python27:42
  • Function Flow in Data Engineering25:19
  • Non-idempotent in Python26:28
  • Pandas vs Spark vs SQL in Data Engineering27:32
  • Pandas Generate27:32
  • Pandas Clean22:47
  • Exception Flow in Python24:01
  • Exception Files in Python25:58
  • Log Levels in Python26:46
  • Python - Snowflake Flow26:28
  • Snowflake Demo25:19
  • REST Basics in Python26:46
  • Class Structure26:14
  • Data Pipeline in Data Engineering25:12
  • Load Data in Data Engineering23:38
  • Pipeline Config in Python29:37
  • Multithreading vs Multiprocessing34:33

Requirements

  • You do not need to have experience

Description

Enter the world of modern data engineering with the Complete Python, SQL & Spark for Data Engineering Course, a comprehensive and hands-on program designed to equip you with the core skills required to build scalable data systems. This course provides a complete roadmap—from learning how to write efficient code to processing massive datasets—making it ideal for beginners as well as professionals looking to advance their careers in data engineering.

The journey begins with Python, where you will develop a strong programming foundation essential for data engineering tasks. You will learn how to work with data structures, handle files, automate workflows, and write clean, maintainable code. As you progress, you will explore practical techniques for data manipulation and transformation, enabling you to work efficiently with real-world datasets.

Next, you will dive into SQL, the backbone of data querying and management. You will master essential and advanced SQL concepts, including joins, aggregations, subqueries, window functions, and query optimization. You will also learn how to design efficient data models and interact with relational databases, ensuring that your data pipelines are both reliable and performant.

Building on these foundations, the course introduces Apache Spark, a powerful big data processing framework used to handle large-scale data across distributed systems. You will learn Spark architecture, understand how distributed computing works, and gain hands-on experience with DataFrames and RDDs. Through real-world examples, you will perform complex transformations and process massive datasets with high efficiency.

If your goal is to master the essential tools of data engineering and work on real-world data challenges, this course is your complete guide to success.

Who this course is for:

  • Everyone who wants to master Data Engineering concepts and become a data engineer

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