Build a Real-World Azure Data Engineering Project
Build a production-style data pipeline using Azure Data Factory, ADLS Gen2, Azure Databricks, PySpark, Delta Lake and Medallion Architecture — and finish with a project you can confidently explain in a Data Engineering interview.
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Stop Just Watching Tutorials
Following someone else's code is very different from solving a real data engineering problem yourself.
The challenge: Many aspiring Data Engineers understand SQL, Python, ADF or Databricks individually, but struggle to combine them into one realistic end-to-end project.
This project starts with a business problem and realistic requirements. You design, build, troubleshoot and explain the solution like a Data Engineer.
What You'll Build
One complete project covering the core engineering patterns used in modern Azure data platforms.
Incremental Ingestion
Build ingestion pipelines that process new and changed data instead of reloading everything.
Medallion Architecture
Organize data into Bronze, Silver and Gold layers with clear responsibilities.
PySpark Transformations
Clean, transform, validate and prepare data using Azure Databricks and PySpark.
Data Quality
Handle duplicates, missing values, invalid records and common quality problems.
Delta Lake
Build reliable Delta tables and understand why lakehouse storage matters.
Error Handling
Think about failed records, pipeline failures, logging and production reliability.
Project Architecture
Follow the data from raw business sources through ingestion, transformation and analytics-ready tables.
Raw
Clean
Business
Work With a Realistic Business Scenario
Imagine you've joined an e-commerce company as a Data Engineer. The business needs a reliable analytics platform built from multiple operational datasets.
What You'll Receive
Everything needed to move from a business requirement to a portfolio-ready implementation.
📁 Realistic Dataset
Multiple connected datasets designed around an e-commerce business.
📋 Business Requirements
Requirements that make you think like an engineer instead of simply copying code.
🏗️ Architecture
Architecture diagrams explaining how each Azure component fits into the solution.
💻 Starter Code
Project structure and starter resources to help you begin without giving away every answer.
✅ Reference Solution
Compare your implementation against a complete solution after attempting the challenges.
🎯 Interview Questions
Practice explaining architecture, decisions, failures, performance and trade-offs.
Learn Like a Data Engineer
The project is designed around doing the work — not passively watching it.
Receive the Requirement
Start with the business problem, datasets and engineering requirements.
Design the Solution
Decide how data should be ingested, stored, transformed and modeled.
Build It
Implement pipelines with ADF, ADLS Gen2, Databricks, PySpark and Delta Lake.
Compare & Improve
Review the reference solution and understand alternative engineering decisions.
Prepare for Interviews
Practice explaining what you built, why you built it that way and how you'd scale it.
Your Final Outcome
Finish with more than another completed tutorial.
Want to Build This Project?
We're preparing the first version of the Real-World Azure Data Engineering Project Kit.
Join the early-access list and tell us what you're struggling with. Your feedback will help shape the first release.
No spam. We'll only contact you about this project and early access.