MS SQL to Databricks Spark ETL Training for Data Engineers
Learn to integrate MS SQL & Databricks and Spark to design reliable ETL workflows that prepare data for modern analytics
Preview this Course
What you'll learn
- Understand how Databricks works and why it is a leading platform for modern data engineering
- Set up, navigate, and manage your Databricks workspace and user interface
- Work confidently with notebooks, files, and Databricks compute clusters
- Improve development speed using productivity shortcuts and essential notebook commands
- File and Notebook ManLearn Lakehouse Architecture and the Medallion (Bronze–Silver–Gold) data design patternagement in Databricks
- Master Delta Lake fundamentals, including ACID transactions and Delta Log operations
- Use Unity Catalog for centralized governance, 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 the Olist dataset from raw Bronze to clean Silver
- Detect duplicates, missing data, schema issues, and apply data quality checks
- Clean and enrich Customers, Sellers, Products, Orders, Order Items, Payments & Reviews data
- Deduplicate and validate geolocation and reference tables in Silver
- Perform analytical transformations for Gold-layer reporting
- Conduct customer distribution, seller metrics, and product category analysis
- Build unified gold-level order analytics and high-quality analytical joins
- Start learning from scratch and learn about every MS SQL Server topic with examples
- Learn SQL basics with SSMS (SQL Server Management Studio)
- Use SQL commands to filter, sort and manipulate strings, dates numerical data from different sources
- User privileges, permission commands and roles
- Learn how to create, alter and drop tables
Description
Welcome to "MS SQL to Databricks Spark ETL Training for Data Engineers" course.
Learn to integrate MS SQL & Databricks and Spark to design reliable ETL workflows that prepare data for modern analytics
MS SQL Server is one of the most widely used relational database systems in the world. It provides a powerful environment for data storage, querying, optimization, and enterprise-level analytics. With T-SQL, you can write complex queries, manage relational structures, and prepare data for downstream ETL workloads.
Databricks is a unified analytics and data engineering platform built on Apache Spark, designed for large-scale data processing, ETL workflows, and collaborative development. It enables efficient data transformation, Delta Lake storage, and enterprise-grade governance via Unity Catalog.
In this course, we will take you through everything you need to know to master data engineering using MS SQL, Databricks, and Apache Spark, supported by diagrams, hands-on examples, and real ETL pipeline development.
Designed for all skill levels, this course takes you step-by-step from beginner concepts to advanced data engineering techniques. With practical demonstrations, clear explanations, and engaging projects, you'll master the essential components of modern ETL workflows.
This course will empower you to build reliable, production-ready data pipelines by fully leveraging MS SQL and Databricks. You’ll gain the skills to clean, extract, transform, validate and optimize data, along with the problem-solving techniques required to tackle real-world ETL challenges—giving you a strong competitive edge in the data engineering field.
Ready to build powerful ETL pipelines with MS SQL and Databricks? This course is the perfect starting point!
What You Will Learn:
ETL Pipeline Architecture (MS SQL & Databricks):Understand how modern ETL workflows operate. Learn SQL-based preprocessing, Databricks notebook logic, and Spark job execution flow.
MS SQL Foundations for Data Engineering:Master SQL queries, joins, subqueries, views, stored procedures, triggers, constraints, indexing, and performance tuning.
Databricks Workspace & Notebooks:Learn how to navigate the Databricks interface, manage databases, use collaborative notebooks, and configure clusters.
Apache Spark Fundamentals:Understand Spark DataFrames, lazy evaluation, transformations, actions, distributed processing, and optimized execution methods.
Delta Lake & Modern Storage Concepts:Learn Delta Lake features such as ACID transactions, Delta Log, schema evolution, upserts, and time travel.
Unity Catalog & Data Governance:Hands-on experience with secure data management, catalogs, schemas, tables, permissions, and lineage.
Data Cleaning & Transformation (Bronze → Silver → Gold):Master medallion architecture using real datasets. Perform deduplication, missing data handling, normalization, validation, and enrichment operations.
SQL + Spark Data Processing:Combine MS SQL preprocessing with Spark transformations for scalable ETL across large datasets.
Performance Optimization (SQL & Spark):Learn SQL indexing, query tuning, execution plans, Spark partitioning, caching, broadcast joins, and optimization best practices.
Deploying ETL Workflows:Understand job scheduling, Databricks Jobs, cluster policies, and automation techniques.
By the end of this course, you'll be confident in building robust and scalable ETL pipelines with MS SQL and Databricks, fully prepared to tackle real-world data engineering projects.
What is Databricks?
Databricks is a cloud-based unified environment built on Apache Spark, designed for large-scale data processing, ETL, and analytics. It offers collaborative notebooks, scalable compute, Delta Lake storage, and strong governance tools.
What is MS SQL Server?
MS SQL Server is a relational database management system used to store structured data, write complex queries, optimize performance, and support enterprise-level analytics and ETL workflows.
What is Apache Spark?
Apache Spark is a distributed data processing engine built for fast and scalable ETL, analytics, streaming and machine learning workloads. Databricks enhances Spark with optimized execution and enterprise-ready features.
Why would you want to take this course?
Our answer is simple: The quality of teaching
OAK Academy based in London is an online education company OAK Academy gives education in the field of IT, Software, Design, development in Turkish, English, Portuguese, and a lot of different language on Udemy platform where it has over 2000 hours of video education lessons.
When you enroll, you will feel the OAK Academy`s seasoned developers' expertise
Video and Audio Production Quality
All our content is created/produced as high-quality video/audio to provide you the best learning experience
You will be,
Seeing clearly
Hearing clearly
Moving through the course without distractions
You'll also get:
Lifetime Access to The Course
Fast & Friendly Support in the Q&A section
Udemy Certificate of Completion Ready for Download
We offer full support, answering any questions
Dive in now into the "MS SQL to Databricks Spark ETL Training for Data Engineers" course.
Learn to integrate MS SQL & Databricks and Spark to design reliable ETL workflows that prepare data for modern analytics
Who this course is for:
- Anyone who wants to learn data engineering through real, end-to-end Databricks workflows
- Aspiring data engineers looking to gain industry-ready experience with Spark,Unity Catalog, and the Databricks ecosystem
- Learners who want to strengthen their Python and SQL skills through practical data engineering projects
- Anyone curious about how large-scale data systems work in real-world organizations
- Those seeking a hands-on guide to building ETL pipelines using the Lakehouse and Medallion (Bronze–Silver–Gold) Architecture
- Students, analysts, or professionals interested in Databricks, Apache Spark, or modern data platforms
- Anyone who wants to learn data engineering through real, end-to-end Databricks workflows
Post a Comment for "MS SQL to Databricks Spark ETL Training for Data Engineers"