
Master end-to-end data engineering with a hands-on ETL project, learning ETL vs ELT, data ingestion with AWS S3, data lake vs data warehouse, and orchestration with Prefect and Docker.
Explore data engineering fundamentals in a structured bootcamp, coding along in Python from requirements and installation to basic concepts and a final exercise, with practical debugging steps.
Install git on your local system to clone the repository and begin work. Learn to download git, run the installer with default settings, and use git bash if desired.
Upload CSV files to a structured S3 raw data folder in the data lake, track progress with a counter, handle errors without stopping the pipeline, and report final upload results.
Explore data processing in a data engineering bootcamp by building ETL pipelines and comparing ETL with ELT, using Python for local extract, transform, and load from an AWS data lake.
Build an etl pipeline extract function that downloads csv files from s3, loads them into data frames, and organizes them in a data sets dictionary for later transformation.
Explore how prefect orchestrates a simple hello world function using flow decorators, automatic logging, and a dashboard to monitor flows, deployments, and tasks.
Build and deploy a data orchestration workflow by creating a work pool, defining workers and deployments, and automating etl tasks with a Prefect flow and yaml configuration.
Data is the new oil—but without the right systems to collect, store, and process it, data quickly becomes unusable. That’s where data engineering comes in. This Data Engineering Bootcamp is designed to take you from foundational concepts to a complete, hands-on project where you’ll build and deploy an end-to-end data pipeline.
We’ll start with the basics of data engineering, exploring what it is, how it differs from roles like analysts and scientists, and why it’s such a critical skill in today’s data-driven world. You’ll learn about the data engineering workflow, data roles, and real-world scenarios through interactive quizzes and activities.
Next, we’ll dive into data architecture—comparing traditional vs. modern approaches, understanding data storage paradigms, and exploring ETL vs. ELT and batch vs. streaming pipelines. You’ll put your knowledge into practice with worksheets and design exercises that reinforce key concepts.
The highlight of the course is the hands-on project, where you’ll:
Ingest raw data into an AWS S3 data lake
Process and transform datasets for analytics
Organize and store results in multiple formats
Orchestrate workflows with Prefect for automation, scheduling, and monitoring
By the end of this course, you’ll not only understand the theory but also gain practical, job-ready experience in building cloud-based data pipelines. Whether you’re an aspiring data engineer, a data analyst looking to level up, or a career changer entering the data field, this bootcamp will give you the confidence and skills to succeed.