Databricks - Master Azure Databricks for Data Engineers (Udemy.com)
Learn Azure Databricks for professional data engineers using PySpark and Spark SQL with an end-to-end capstone project
Created by: Learning Journal
Last updated May 2026
What you will learn
- Databricks in Azure Cloud
- Working with DBFS and Mounting Storage
- Unity Catalog - Configuring and Working
- Unity Catalog User Provisioning and Security
- Working with Delta Lake and Delta Tables
- Manual and Automatic Schema Evolution
- Incremental Ingestion into Lakehouse
- Databricks Autoloader
- Delta Live Tables and DLT Pipelines
- Databricks Repos and Databricks Workflow
- Databricks Rest API and CLI
- Capstone Project
How it compares
Against the other 461 rated Udemy courses in Data Science on CourseDuck:
- Rating4.6 out of 5higher than 67% of them
- Reviews4,027#52 of 462 by number of reviews
- Length17.6 hourslonger than 76% of them
- Last updatedMay 2026more recently than 75% of them
Course Description
About the Course
I am creating Databricks - Master Azure Databricks for Data Engineers using the Azure cloud platform. This course will help you learn the following things.
Databricks in Azure Cloud
Working with DBFS and Mounting Storage
Unity Catalog - Configuring and Working
Unity Catalog User Provisioning and Security
Working with Delta Lake and Delta Tables
Manual and Automatic Schema Evolution
Incremental Ingestion into Lakehouse
Databricks Autoloader
Delta Live Tables and DLT Pipelines
Databricks Repos and Databricks Workflow
Databricks Rest API and CLI
Capstone Project
This course also includes an End-To-End Capstone project. The project will help you understand the real-life project design, coding, implementation, testing, and CI/CD approach.
Who should take this Course?
I designed this course for data engineers who are willing to develop Lakehouse projects following the Medallion architecture approach using the Databrick cloud platform. I am also creating this course for data and solution architects responsible for designing and building the organization’s Lakehouse platform infrastructure. Another group of people is the managers and architects who do not directly work with Lakehouse implementation. Still, they work with those implementing Lakehouse at the ground level.
Spark Version used in the Course.
This course uses Databricks in Azure Cloud and Apache Spark 3.5. I have tested all the source codes and examples used in this course on Azure Databricks Cloud using Databricks Runtime 13.3.
Instructor Details
- 4.6 Rating
4,027 Reviews
Learning Journal
Learning Journal is a small team of people passionate about helping others learn and grow in their careers by bridging the gap between their existing and required skills. In our quest to fulfill this mission, we are authoring books, publishing technical articles, and creating training videos to help IT professionals and students succeed in the industry.
Together we have over 40+ years of experience in IT as a developer, architect, consultant, trainer, and mentor. We have worked with international software services organizations on various data-centric and Bigdata projects.
Learning Journal is a team of firm believers in lifelong continuous learning and skill development. To popularize the importance of lifelong continuous learning, we started publishing free training videos on our YouTube channel. We conceptualized the notion of continuous learning, creating a journal of our learning under the Learning Journal banner.
We authored various skill development courses, training, and technical articles since the beginning of the year 2018.
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Reviews
By Shubham Chandrakant Rokade on 8/26/2026
Very good course however there are some changes needs to apply with respect to new databricks ui
By Ishant Arora on 6/22/2026
Course is outdated. Some of the methods used are legacy methods which does not work with Databricks anymore, making it hard to follow.
By Hanumantha Reddy on 6/15/2026
Sir, this explanation about Auto Loader failing once during schema evolution and succeeding on retry really helped me understand the production behavior. Earlier I thought it was just a random failure, but now I understand that Auto Loader updates the schema metadata first and then requires a restart to process with the new schema. Thank you for explaining the real-world handling and retry mechanism
By Sivasri on 5/1/2026
Very informative, and the lectures are so easy to follow. The course content is very well planned, even for those who are beginners to Databricks. Thank you. I learnt a lot and am able to work independently on the Databricks projects.
By Udemy User on 4/8/2026
Well explain, If we are studying any topic then explain well why we need this, tells all concepts very clearly. Very helpfull for me.
By Rakshitha Huliyar on 2/27/2026
Thank you for simple and straightforward teaching Sir, Appreciate all your efforts and patience in helping us learn the topics. !
By Amit Kumar on 1/15/2026
Salute you sir. Last 4 lectures in the section 7 deserve 7 stars. AWESOME
By Jose Gheevarghese on 1/13/2026
The flow of the course is good. Started from the basic and builds on top of it
By Anonymized User on 12/5/2025
Content wise it is great! It might be useful if final project could have been explained with results and how it is integrated with each other. But overall it is very helpful to step from beginners to advanced level.
By Nitesh Chaturvedi on 12/3/2025
Decent Course but lacks in-depth explanation as to why we're doing what we're doing. And the code part is mostly rushed explanation. Some of the options and Screen UI has changed and the videos have not been updated which makes a bit hard to follow. Overall a decent course but can't teach you everything from start to finish. Good for understanding basics
Quality Score
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Overall Score : 92 / 100







