Description
Databricks | Spark ETL & Delta Lake Data Engineering Mastery is a course that provides strong, job-ready skills in modern data engineering using Apache Spark and the Databricks platform, published by Udemy Online Academy. This is a comprehensive course designed to build strong, job-ready skills in modern data engineering using Apache Spark and the Databricks platform. The course guides learners through designing and implementing scalable ETL pipelines, efficiently processing large datasets, and managing reliable data lakes using Delta Lake. With a strong focus on real-world workflows, performance optimization, and best practices, participants gain hands-on experience transforming raw data into analysis-ready datasets while using Databricks for collaboration, monitoring, and data engineering at production level.
In this comprehensive course, you’ll learn how to transform raw datasets into clean, reliable, and analysis-ready data using the full Medallion architecture (Bronze → Silver → Gold), while developing the practical skills expected of industry-ready data engineers. Databricks combines the processing power of Apache Spark with the flexibility of Lakehouse, enabling professionals to efficiently manage, clean, and analyze data. Whether you’re an aspiring data engineer, a student, or a working professional, this course will equip you with the mindset, techniques, and practical skills to build modern data pipelines on one of the world’s most in-demand platforms.
What you will learn in Databricks | Spark ETL & Delta Lake Data Engineering Mastery:
- Deep dive into the Databricks UI
- How Databricks works as an integrated platform
- File and notebook management in Databricks
- Databricks compute options and cluster settings
- Databricks notebook environment and essential commands
- Productivity shortcuts for faster development
- Lakehouse architecture basics
- Understanding medallion tiers (bronze, silver, gold)
- ACID transactions and Delta Log requirements
- From DBFS to Unity Catalog
- Unity Catalog tiers and data management principles
- Managed vs. external tables
- Creating catalogs, schemas, tables, and volumes
- Getting started with ETL and Apache Spark
- Understanding the Olist data model
- Bronze tier ETL basics
- And…
Course specifications
Publisher: Udemy
Instructors: Oak Academy , OAK Academy Team and Ali̇ CAVDAR
Language: English
Level: Introductory to Advanced
Number of Lessons: 83
Duration: 13 hours and 7 minutes
Course topics
Databricks | Spark ETL & Delta Lake Data Engineering Mastery Prerequisites
A working computer (Windows, Mac, or Linux)
A stable internet connection to access Databricks
Basic understanding of Python (functions, loops, variables — just the essentials)
Basic understanding of SQL (basic queries like SELECT, WHERE, JOIN are enough)
Interest in data engineering and real-world data pipelines
Curiosity about modern cloud platforms and large-scale ETL workflows
Motivation to build complete end-to-end pipelines using Databricks & Apache Spark
No prior experience with Databricks, Spark, or the Lakehouse required
Just you, your keyboard, and your passion for becoming a data engineer!
Pictures
Databricks | Spark ETL & Delta Lake Data Engineering Mastery introduction video
Installation guide
After Extract, watch with your favorite Player.
Subtitle: None
Quality: 720p
Downloadly link
Rapidgator link
File password (s): www.downloadly.ir
Size
4.5 GB
