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.
