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Keep shoppingAzure Databricks for Beginners | PySpark, Unity Catalog &ETL
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20 students
Azure Databricks for Beginners | PySpark, Unity Catalog &ETL
Learn Azure Databricks, PySpark, Unity Catalog, ETL Pipelines, Spark Architecture, Azure SQL & ADLS Gen2
Created byMallaiah Somula
Last updated 5/2026
English
What you'll learn
- Understand Azure Databricks architecture, workspaces, clusters, notebooks, compute options, and real-world data engineering workflows
- Build PySpark DataFrame transformations using select, filter, joins, aggregations, null handling, and ETL processing techniques
- Implement Unity Catalog, Managed Identity, Azure Key Vault, and secure ADLS Gen2 access in Azure Databricks projects
- Develop end-to-end ETL pipelines using Azure Databricks, Azure SQL Database, ADLS Gen2, JDBC, and reusable utility functions
Explore related topics
Course content
7 sections • 27 lectures • 35h 0m total length
Requirements
- Basic computer knowledge and familiarity with using a web browser is enough to start this course
- No prior Databricks or PySpark experience is required. Everything will be explained from beginner level
- Basic understanding of databases, SQL, or data concepts will be helpful but is not mandatory
- A free Azure account or Databricks Community Edition can be used to practice the hands-on exercises
Description
Welcome to the Azure Databricks for Beginners course designed for aspiring data engineers, ETL developers, students, and working professionals who want to build strong hands-on skills in Azure Databricks and PySpark from scratch.
In this course, you will learn Azure Databricks step by step with practical examples, real-time scenarios, interview-focused concepts, and end-to-end ETL project implementation. The course starts with Databricks fundamentals including workspace setup, clusters, notebooks, compute options, workflows, and job execution concepts.
You will then build strong Python fundamentals required for PySpark development including strings, lists, tuples, sets, dictionaries, functions, lambda expressions, modules, and packages using real-time data engineering examples.
The course also covers important Spark and PySpark concepts such as Spark Architecture, DAG, partitions, parallelism, lazy evaluation, transformations, actions, joins, aggregations, CSV processing, and PySpark UDFs.
One of the key highlights of this course is learning modern Azure Databricks features including Unity Catalog, Azure Key Vault integration, Managed Identity access, centralized governance, and secure ADLS Gen2 integration without mounts.
You will also learn how to connect Azure Databricks with Azure SQL Database using JDBC and build a complete end-to-end ETL pipeline using PySpark, Azure SQL Database, and ADLS Gen2.
By the end of this course, you will have practical hands-on experience in Azure Databricks and PySpark which will help you work on real-world data engineering projects and prepare for Databricks and Azure Data Engineering interviews.
Who this course is for:
- Beginners who want to learn Azure Databricks, PySpark, and modern data engineering from scratch with hands-on examples
- Data engineers, ETL developers, and SQL developers looking to transition into Azure Databricks and Spark technologies
- Students and working professionals preparing for Databricks, PySpark, and Azure Data Engineering interviews
- Anyone interested in building real-world ETL pipelines using Azure Databricks, Unity Catalog, Azure SQL, and ADLS Gen2