Databricks for Data Engineers: Full Curriculum (Structured) | Udemy [Update 04/2026]
English | Size: 3.85 GB
Genre: eLearning[/center]

Build real-world pipelines, optimize data, apply governance, and create dashboards with Databricks & BI tools

What you'll learn
Build end-to-end data pipelines using modern tools like Databricks, Spark, and SQL
Understand and implement ETL & ELT workflows for batch and streaming data processing
Design scalable architectures using the Medallion (Bronze, Silver, Gold) framework
Optimize data performance using partitioning, caching, query tuning, and cost optimization techniques
Implement data governance and security with concepts like Unity Catalog, RBAC, and data lineage
Create business-ready datasets (Gold tables) for analytics and reporting
Run efficient queries using SQL Endpoints and improve query performance
Connect data to Power BI/Tableau and build dashboard-ready data pipelines
Apply real-world best practices used by professional data engineers
Gain job-ready skills to work as a Data Engineer in modern data platforms

"This course contains the use of artificial intelligence"

Welcome to the ultimate Data Engineering Bootcamp, designed to take you from foundational concepts to building production-grade data systems used in real-world companies.

In this course, you won't just learn theory-you'll build end-to-end data pipelines, work with Databricks, and deliver business-ready dashboards using modern tools and best practices.

We start with the fundamentals of data engineering, including the Medallion Architecture (Bronze, Silver, Gold), and gradually move into advanced topics like ETL vs ELT, batch and streaming pipelines, and incremental processing.

You'll learn how to work with different data formats like CSV, JSON, and Parquet, and how to design efficient pipelines using Apache Spark. We'll also cover how to build optimized storage using Delta Lake, ensuring your data is reliable, scalable, and ready for analytics.

As you progress, you'll master data optimization techniques such as partitioning, query optimization, caching, and cost optimization strategies like cluster sizing and autoscaling. These are critical skills used by professional data engineers to improve performance and reduce cloud costs.

We also go deep into data governance and security, where you'll learn how to use Unity Catalog, implement role-based access control (RBAC), and manage data lineage to track how data flows through your system.

Once your data is prepared, you'll move into the analytics layer-learning how to create Gold tables that are structured for business use. You'll use SQL Endpoints to run analytical queries and understand how to optimize them for performance.

Finally, you'll connect everything to BI tools like Power BI and Tableau, creating dashboard-ready data and building visualizations that drive real business decisions.

What Makes This Course Unique?

This is not just another tutorial-based course. You will:

Build real-world data pipelines from scratch

Work with Databricks, Spark, and Delta Lake

Learn industry-standard architecture used in modern companies

Apply performance optimization techniques used in production

Implement data governance, security, and access control

Deliver end-to-end analytics solutions with dashboards

By the End of This Course, You Will Be Able To:

Design and implement scalable data pipelines

Understand and apply ETL and ELT workflows

Optimize data using partitioning, caching, and query tuning

Implement data governance and security best practices

Build Gold layer datasets for business analytics

Run efficient queries using SQL Endpoints

Create dashboard-ready data for BI tools

Deliver insight-driven analytics solutions

Who this course is for:
Beginners who want to start a career in Data Engineering and learn industry-relevant tools
Students or graduates in Computer Science, IT, or related fields looking to build practical skills
Data Analysts who want to move into data engineering and understand how pipelines work
Software developers interested in working with big data, Spark, and modern data platforms
Professionals looking to upskill in Databricks, ETL/ELT, and cloud-based data systems

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