Data Engineering with Python, SQL & Spark Full Stack Course

Name: Data Engineering with Python, SQL & Spark Full Stack Course
Rating: 4.6 (51 reviews)

Master Data Engineering from scratch to advanced and Learn Python, SQL, GIT, DSA and Docker and many more with Projects

Created byKasi Gopi

Last updated 7/2026

English

What you'll learn

Learn the fundamentals of data engineering, including databases, data warehousing, ETL pipelines, and big data concepts from beginner to advanced level.
Build scalable data pipelines using modern tools and technologies such as SQL, Python, Apache Spark, Hadoop, Kafka, and cloud platforms.
Master data processing, transformation, storage, and analytics techniques for handling real-world structured and unstructured data.
Develop hands-on experience through practical projects, cloud integration, and industry-standard workflows to become a job-ready data engineer.

Course content

6 sections • 69 lectures • 47h 2m total length

Introduction6:13
Master data engineering with Python, SQL and Spark by building scalable pipelines, cleaning data, and delivering high-quality data for analytics across data warehouses and data lakes.

Getting Started with Python30:58
Primitive Types32:17
Control Flow37:38
Master control flow techniques within data engineering using Python, SQL, and Spark to structure and optimize data pipelines.
Functions38:10
Data Structures1:17:31
Explore data structures within the data engineering context, highlighting how Python, SQL, and Spark tools organize and optimize data workflows.
Exceptions22:12
Learn how to handle exceptions in Python using try/except, else and finally blocks, with the with statement for resource management, and when to raise exceptions for performance.
Python Classes Part 136:55
Explore Python classes as the first part of the data engineering full stack course, building foundational skills for data-driven programming with Python.
Python Classes Part 246:02
Modules17:21
Learn how to organize Python code into modules and packages, create ecommerce subpackages, use absolute and relative imports, and employ dir and the __name__ == '__main__' pattern.
Python Standard Library59:42
Python Package Index45:42
Popular Python Packages54:08
Building Web Applications with Django Part - 11:01:06
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Building Web Applications with Django Part - 257:32
Explore how to build web applications with Django, in part 2 of the full stack data engineering course, leveraging Python, SQL, and Spark.
Machine Learning with Python46:48
Explore machine learning with Python within data engineering workflows, integrating SQL and Spark to build end-to-end data solutions.
Dice Rolling Game Project in Python7:24
Build a dice rolling game in Python to practice problem solving, loops, and user input, generating two random numbers and guiding flow with yes/no prompts.
Number Guessing Game and Rock Paper Scissor Projects in Python33:53
Currency Converter and Quiz Game Projects in Python39:57
Tic Tac Toe Game Project in Python29:20
Todo List and Simple Text Editor Projects in Python35:03
Pig Dice Game and Cows and Bulls Game Projects in Python32:29
Build two classic Python games—pig dice and cows and bulls—learn to break down problems, loop until a winner, and implement game logic with functions and user input.
Password Strength Checker and Password Generator projects in Python19:21
Word Guessing Game and Slot Machine Game Projects in Python40:10
ATM Simulation Project in Python Programming36:24
Importance of Python Programming6:47
Discover why Python ranks as a versatile, beginner-friendly language for web development, data science, automation, and AI, supported by libraries like Pandas, Matplotlib, Plotly, and Scikit-learn.

Getting Started with SQL24:26
Master the fundamentals of relational databases and sql by installing mysql, using mysql workbench, and building tables and relationships to query data across projects.
Retrieving Data From a Single Table57:12
Learn how to retrieve data from a single table using Python, SQL, and Spark, enabling efficient data querying in data engineering workflows.
Retrieving Data From Multiple Tables48:01
Master techniques for retrieving data from multiple tables using Python, SQL, and Spark for data engineering workflows.
Inserting, Updating, and Deleting Data41:50
Summarizing Data in SQL30:22
Writing Complex Query38:21
Essential MySQL Functions31:19
Views in SQL18:17
Learn how to create and use views in SQL to simplify complex queries, reuse logic, and update data through updatable views such as sales by client and invoices with balance.
Stored Procedures47:59
Learn how stored procedures streamline data workflows by creating and using them with Python, SQL, and Spark, empowering efficient data engineering practices.
Triggers and Events20:51
Transactions and Concurrency42:35
Explore how transactions and concurrency are managed in data engineering with Python, SQL, and Spark.
Data Types24:02
Designing Databases Part 159:38
Design databases as part of the data engineering with python, sql & spark full stack course, focusing on foundational database design concepts in a beginner-friendly part 1.
Designing Databases Part 259:58
Designing databases part 2 equips you with practical database design concepts within the data engineering with Python, SQL & Spark full stack course.
Indexing for High Performance52:26
Explore indexing techniques to boost performance in data engineering workflows, using Python, SQL, and Spark within a full stack data course.
Securing Databases in SQL14:13
Learn to secure MySQL databases by creating restricted user accounts, assigning precise privileges for applications and admins, and using grants, revokes, and password practices.

Getting Started with DSA17:33
Explore core data structures: arrays, linked lists, stacks, queues, hash tables, trees, and graphs, while mastering big O notation, algorithm analysis, and interview-focused problem solving for scalable coding.
Arrays29:18
Linked Lists44:44
Master linked lists within the data engineering context, leveraging Python, SQL, and Spark to model and traverse data structures.
Stacks41:58
Queues51:48
Learn how queues streamline data engineering tasks using Python, SQL, and Spark, enabling efficient data processing and workflow management.
Hash Tables1:06:06
Explore hash tables and their use in data engineering workflows. Apply concepts with Python, SQL, and Spark to optimize lookups and data management.
Binary Trees1:14:20
Explore binary trees and their use in data engineering workflows, using Python, SQL, and Spark to build scalable data solutions.
AVL Trees48:46
Explore AVL trees in the data engineering with Python, SQL & Spark full stack course, highlighting their use in efficient data organization and retrieval.
Heaps54:04
Trees46:32
Graphs1:06:45
Undirected Graphs58:11
Explore undirected graphs within data engineering using Python, SQL, and Spark to understand graph structures and their applications in data workflows.
Sorting Algorithms1:12:25
Explore sorting algorithms within data engineering, applying Python, SQL, and Spark to organize data efficiently.
Searching Algorithms34:09
String Manipulation Algorithms56:21
Explore string manipulation algorithms in the data engineering course, applying Python, SQL, and Spark to efficiently process and transform text data.

Getting Started with Git13:58
Master git fundamentals, from installation and configuration to snapshots, branching, merging, and history rewriting, enabling collaborative work with GitHub via command line and GUI tools like VS Code and GitKraken.
Creating Snapshots57:53
Create snapshots to capture data states within data engineering workflows. Master the use of Python, SQL, and Spark in this full stack course.
Browsing History42:53
Explore how to manage and analyze user browsing history data using Python, SQL, and Spark techniques within a data engineering workflow.
Branching1:11:35
Explore branching concepts in data engineering with Python, SQL, and Spark, as part of the data engineering with Python, SQL and Spark full stack course.
Collaboration16:40
Rewriting History44:31
Delve into rewriting history using data engineering techniques with Python, SQL, and Spark to transform data into scalable, actionable insights.

Getting Started with Docker26:18
Master docker from basics to advanced concepts and deploy a stack app. Learn the workflow: write a docker file, build an image, run containers, and push to Docker Hub.
The Linux Command Line1:03:08
Master the Linux command line for data engineering with Python, SQL, and Spark in this course.
Building Images1:00:38
Build images as part of the data engineering course, leveraging Python, SQL, and Spark to understand image handling in data pipelines.
Working with Containers21:42
Running Multi-container Applications46:45
Master running multi-container applications within the data engineering course, aligning the approach with practical needs of data projects.
Deploying Applications31:16

Requirements

You will learn from scratch and You do not need to have any experience in this course

Description

The Complete Data Engineering Bootcamp from Beginner to Advanced is a comprehensive and industry-focused program designed to transform beginners into confident and skilled data engineering professionals. Whether you are a student, software developer, database administrator, analyst, or someone curious about big data technologies, this course provides a step-by-step roadmap to mastering modern data engineering concepts and tools used by top technology companies worldwide.

In this bootcamp, you will begin with the fundamentals of data engineering, understanding how data flows through modern systems and how organizations collect, process, store, and analyze massive amounts of information. You will learn essential database concepts, SQL programming, data modeling, and data warehousing techniques that form the foundation of every successful data engineering pipeline.

This bootcamp emphasizes practical learning through projects, exercises, and real-world scenarios. You will build data pipelines, automate workflows, manage streaming data, optimize databases, and work with structured as well as unstructured data. By the end of the course, you will understand how enterprise-level data systems are designed and maintained efficiently.

By completing this bootcamp, you will gain the confidence and practical skills required to pursue careers such as Data Engineer, Big Data Engineer, ETL Developer, Cloud Data Engineer, Analytics Engineer, or Data Platform Developer. You will also develop a strong portfolio of projects that demonstrate your expertise to employers and clients.

Who this course is for:

It is for anyone who wants to master Data Engineering from beginner to Advanced

Data Engineering with Python, SQL & Spark Full Stack Course

What you'll learn

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Course content

Introduction1 lecture • 6min

Python Programming with Projects for Data Engineering25 lectures • 15hr 45min

SQL for Data Engineering16 lectures • 10hr 12min

DSA for Data Engineering15 lectures • 12hr 43min

GIT for Data Engineering6 lectures • 4hr 8min

Docker for Data Engineering6 lectures • 4hr 10min

Requirements

Description

Who this course is for: