Ultimate AWS Data Engineering Bootcamp - 15 Real-World Labs

Master Data Engineering on AWS with Airflow, Redshift, Glue, Spark, ECS & More

Created by Siddharth Raghunath

Explore advanced data engineering on AWS by working through 15 practical labs that mirror real business scenarios. Build skills in orchestration, ETL, real-time streaming, and CI/CD using a range of AWS tools. Designed for those with Python and basic cloud knowledge, you'll gain hands-on experience architecting and managing cloud data solutions.

Packt | May 2025 | 480 min

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LevelExpert

CategoriesData Engineering, Data Warehousing and Big Data Processing Frameworks, Amazon Web Services, Python

1Course Introduction
2Lab - Batch data processing of music streams using Airflow & Redshift
3Lab - Distributed music streams processing using Airflow, Spark & DynamoDB
4Lab - ETL for Rental apartments using Step Functions, AWS Glue, and Redshift
5Lab - Build a datalake for rental vehicles store using EMR, S3 and Athena
6Lab - Build Event driven pipelines for E-Commerce using ECS and Step Functions
7Lab - Build a lakehouse for an E-Commerce store using Pyspark delta tables and S3
8Lab - Event driven data processing for Taxi trips using Lambda and Kinesis
9Lab - Process mobile network logs in real time using Pyspark & Streamlit on ECS
10Lab - CI/CD for AWS Services using GITHUB ACTIONS
11Lab - Real time data ingestion of clickstreams using Kinesis Firehose and Redshift
12Assignment 1 - Setup MySQL Database in AWS Aurora RDS
13Assignment 2 - Build a lakehouse on S3 for Commercial flights dataset
14Assignment 3 - Offer dynamic discounts to E-Commerce users using Real Time Events
15Assignment 4 - Setup real time Pyspark streaming job for Spotify songs metrics
16Assignment 5 - Automate deployment of Lambda functions using Github actions

What You Will Learn

You will work through a series of hands-on projects that reflect real industry challenges. Each project guides you through coding, deploying, and troubleshooting data pipelines using AWS services. By focusing on practical applications, you'll reinforce your understanding of cloud-native architecture and develop solutions that are ready for production environments.

Key Features

Design and automate data workflows using Airflow, Step Functions, and AWS Glue
Build and deploy scalable ETL and real-time pipelines with Redshift, EMR, and Kinesis
Implement CI/CD practices for AWS data services using GitHub Actions and streamline deployments

Target Audience

If you're an aspiring data engineer, cloud developer, or a professional aiming to move into AWS data engineering, this path is for you. You'll need a foundation in Python and some cloud experience. It's ideal if you want to deepen your expertise in building, automating, and deploying data solutions on AWS for real-world applications.

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