Description
MS SQL to Databricks Spark ETL Training for Data Engineers is a course on how to move, transform, and process data from Microsoft SQL Server environments to modern Databricks and Apache Spark platforms, published by Udemy Online Academy. Individuals learn how to design efficient ETL pipelines, work with large-scale data processing frameworks, and apply best practices to build reliable data engineering solutions. Learn how to integrate MS SQL, Databricks, and Spark to design reliable ETL workflows that prepare data for modern analytics. The course covers SQL data mining, Spark programming, data transformation, Delta Lake workflows, Databricks, optimization techniques, and cloud-based data processing concepts.
In this course, we’ll walk you through everything you need to master data engineering using MS SQL, Databricks, and Apache Spark, supported by diagrams, practical examples, and real-world ETL pipeline development. Designed for all skill levels, this course takes you step-by-step from beginner concepts to advanced data engineering techniques. With hands-on demonstrations, clear explanations, and engaging projects, you’ll master the essential components of modern ETL workflows. You’ll gain the skills to clean, extract, transform, validate, and optimize data, along with the problem-solving techniques needed to tackle real-world ETL challenges – giving you a strong competitive edge in the data engineering field.
What you will learn in MS SQL to Databricks Spark ETL Training for Data Engineers:
- Understand how Databricks works and why it is a leader in modern data engineering
- Set up, navigate, and manage the Databricks workspace and user interface
- Work confidently with Databricks notebooks, files, and compute clusters
- Improve development speed with productivity shortcuts and essential notebook commands
- My Files and Notebooks
- Learn the Lakehouse architecture and Medallion data design pattern (Bronze-Silver-Gold) in Databricks
- Master Delta Lake principles, including ACID transactions and Delta Log operations
- Use Unity Catalog for centralized management, permissions, and data organization
- Create and manage catalogs, schemas, tables, and volumes
- Build ETL pipelines using Apache Spark and apply them to real datasets
- Explore and transform Olist datasets from raw bronze to clean silver
- Identify duplicate data, missing data, schema issues, and apply quality checks Data
- and ….
Course specifications
Publisher: Udemy
Instructors: Oak Academy ,OAK Academy Team and Ali̇ CAVDAR
Language: English
Level: Introductory to Advanced
Number of Lessons: 125
Duration: 18 hours and 26 minutes
Course topics
MS SQL to Databricks Spark ETL Training for Data Engineers Prerequisites
Just you, your keyboard, and your passion for becoming a data engineer!
No prior experience with Databricks, Spark, or the Lakehouse required
Motivation to build complete end-to-end pipelines using Databricks & Apache Spark
Curiosity about modern cloud platforms and large-scale ETL workflows
Interest in data engineering and real-world data pipelines
Basic understanding of Python (functions, loops, variables — just the essentials)
A stable internet connection to access Databricks
A working computer (Windows, Mac, or Linux)
Pictures
MS SQL to Databricks Spark ETL Training for Data Engineers introduction video
Installation guide
After Extract, watch with your favorite Player.
Subtitle: None
Quality: 1080p
Downloadly link
Rapidgator link
File password (s): www.downloadly.ir
Size
4.9 GB
