Azure Databricks & Spark for Data Engineers:Hands-on Project

Course Fully Refreshed for 2026

This course has been completely rebuilt for 2026 using the latest Azure Databricks features and best practices.

Instead of relying on legacy approaches such as Hive Metastore and external orchestration tools, this course focuses on modern Databricks capabilities like Unity Catalog, Lakeflow Jobs, Databricks SQL Dashboards, and Genie.

Welcome!

In this course, you will build a complete end-to-end data engineering project using Azure Databricks and Apache Spark based on Formula 1 Motor Racing data.

You won’t just learn individual concepts. You will design and implement a cloud data platform from scratch, following the same approach used in real-world data engineering and data platform projects.

What You Will Build

Throughout the course, you will:

• Design a modern Data Lakehouse architecture using Azure Databricks

• Implement the Medallion Architecture (Bronze, Silver, Gold) for scalable data pipelines

• Ingest, transform, and model data using Apache Spark (PySpark and Spark SQL)

• Store and manage data using Delta Lake in Databricks

• Organise and govern data using Unity Catalog in Azure Databricks

• Build and orchestrate pipelines using Lakeflow Jobs in Databricks

• Create analytical views and dashboards using Databricks SQL and Dashboards

• Enhance the pipeline with incremental data processing using Delta Lake

By the end of the course, you will have built a production-ready data engineering pipeline on Azure Databricks.

Technologies You Will Use

As part of building the project, you will learn:

• Azure Databricks

• Apache Spark using PySpark and Spark SQL

• Delta Lake and modern Lakehouse architecture

• Unity Catalog for data governance and organisation in Databricks

• Databricks SQL and Dashboards for analytics and reporting

How You Will Learn

This is a hands-on, project-based Azure Databricks course.

• You will build the solution step by step

• Concepts are explained in the context of a real-world project

• Each section builds on the previous one

This approach ensures that you not only understand the concepts, but also know how to apply them in real-world data engineering scenarios.

I value your time as much as I do mine. So, I’ve designed this course to be focused, practical, and to the point. The lessons are explained in simple English, without unnecessary jargon, and we start from the basics. By the end of the course, you will be confident building real-world data engineering solutions.

How This Course Supports Certification Preparation

This course can help you build many of the core skills required for the following certifications:

• Databricks Certified Data Engineer Associate

• Databricks Certified Associate Developer for Apache Spark

• Microsoft Exam DP-750: Implementing Data Engineering Solutions Using Azure Databricks

• Databricks Certified Data Engineer Professional

The hands-on project will strengthen your practical understanding of key Databricks and Spark concepts tested in these exams.

However, this course is not designed as a certification preparation course and does not cover all exam topics.

What’s Included (and What’s Not)

• This course focuses on core Spark and Databricks concepts

• It does not cover Spark Streaming, Spark ML, and Lakeflow Declarative Pipelines

• Spark is taught using PySpark and Spark SQL (not Scala or Java)

Final Outcome

By the end of this course, you will have built a complete, production-ready data engineering solution using Azure Databricks and Spark, and gained the confidence to apply these skills in real-world projects.