Learn Databricks from Spark ETL to Unity Catalog and Medallion pipelines to build scalable, high-impact data workflows
What you'll learn
Course Overview & Learning Path
Exam Guide Breakdown
What Databricks Is and Why It Matters for Data Engineering
Creating and Navigating Your Databricks Environment
Databricks User Interface Deep Dive
How Databricks Works as a Unified Platform
File and Notebook Management in Databricks
Databricks Compute Options & Cluster Settings
Databricks Notebook Environment & Essential Commands
Productivity Shortcuts for Faster Development
Lakehouse Architecture Fundamentals
Understanding the Medallion Layers (Bronze, Silver, Gold)
ACID Transactions & Delta Log Essentials
From DBFS to Unity Catalog
Unity Catalog Layers & Data Governance Fundamentals
Managed vs External Tables
Creating Catalogs, Schemas, Tables & Volumes
Getting Started with ETL and Apache Spark
Understanding the Olist Data Model
Bronze Layer ETL Foundations
Exploring Bronze DataFrames
External Tables & Raw Data Access
Detecting Duplicate Keys in Bronze
Missing Value Profiling in Bronze
Final Checks Before Moving to Silver
Cleaning & Normalizing the Customers Table
Transforming the Sellers Table
Cleaning & Enriching the Products Table (All Lessons Combined)
Time, Quality & Missing Data Management in Orders Table (All Lessons Combined)
Order_Items Transformation & Quality Checks (All Lessons Combined)
Payments Data Validation & Transformation (All Lessons Combined)
Building the Silver Version of Order Reviews (All Lessons Combined)
Geolocation Data Cleaning & Deduplication (All Lessons Combined)
Preparing Clean Reference Tables in Silver
Customer Distribution Analysis
Seller Metrics & Pareto Analysis
Analyzing Product Categories by Weight, Volume & Density
Understanding Gold Layer Analytical Stories
Unified Order Gold Analytics (All Lessons Combined)
Designing Analytical Joins for High-Quality Insights
Requirements
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!
Description
Welcome to "Databricks | Spark ETL & Delta Lake Data Engineering Mastery" course.Learn Databricks from Spark ETL to Unity Catalog and Medallion pipelines to build scalable, high-impact data workflowsIn today's data-driven world, the ability to build scalable data pipelines using modern cloud platforms is a true superpower—and nowhere is this more impactful than mastering Databricks, Apache Spark, and the Lakehouse Architecture. In this comprehensive course, you will learn how to transform raw datasets into clean, reliable, analytics-ready data using the full Medallion Architecture (Bronze → Silver → Gold), while developing practical skills expected from industry-ready data engineers.Databricks combines the processing power of Apache Spark with the flexibility of the Lakehouse, enabling professionals to manage, clean, and analyze data efficiently. Whether you're an aspiring data engineer, a student, or a working professional, this course equips you with the mindset, techniques, and hands-on skills to build modern data pipelines on one of the most in-demand platforms in the world.Why This Course?Building data pipelines in real organizations is messy. Raw datasets contain inconsistencies, missing values, duplicates, and other real-world challenges. Databricks solves these problems by combining Apache Spark's distributed computing capabilities with enterprise-grade governance tools like Unity Catalog.In this course, you will learn step-by-step how to clean, transform, validate, and analyze data while mastering tools such as:Build end-to-end data pipelines using Apache Spark on DatabricksApply the Medallion Architecture (Bronze → Silver → Gold) confidentlyUse Unity Catalog for secure and scalable data governanceClean, transform, enrich, and analyze real-world datasetsApply data quality checks, normalization, and advanced Spark operationsWork with notebook workflows and Databricks compute efficientlyCreate analytical datasets ready for dashboards, BI tools, or machine learningDevelop the mindset and skills of a professional data engineer working with complex, production-level data systemsYou will build a complete end-to-end pipeline—from raw ingestion to high-value analytics—just like a professional data engineer working in cloud environments today.By the end, you won't just understand Databricks. you will think like a data engineer.Why Mastering Databricks & Spark MattersDatabricks and Apache Spark are at the heart of modern data engineering. With companies shifting to the Lakehouse model, professionals who understand Spark transformations, Delta Lake reliability, and Unity Catalog governance are in extremely high demand.This course gives you:The technical foundation to work with big dataThe practical experience to build scalable pipelinesThe confidence to operate in real-world cloud environmentsWhether you want to work as a Data Engineer, Analytics Engineer, or Cloud Data Specialist, these skills define the future of the industry.What is Databricks and how is it used in modern data engineering?Databricks is a cloud-based data engineering platform that integrates Apache Spark for high-performance ETL processing. It allows data engineers to build scalable data pipelines, manage Delta Lake tables with ACID transactions, and implement the Medallion Architecture (Bronze → Silver → Gold) to transform raw datasets into analytics-ready data. Databricks also provides notebook workflows, data governance with Unity Catalog, and tools to handle real-world data challenges like inconsistencies, missing values, and duplicates, making it a comprehensive solution for modern data workflows.Why is learning Apache Spark on Databricks essential for data engineers?Learning Apache Spark on Databricks is essential because it enables data engineers to process massive datasets efficiently using distributed computing. Spark on Databricks supports parallelized transformations, advanced data cleansing, and real-time analytics. Data engineers can implement Bronze, Silver, and Gold pipelines, apply data quality checks, enrich datasets, and prepare high-value analytical data for dashboards, BI tools, or machine learning models. Mastering Spark on Databricks provides the practical skills and industry-ready experience required to handle complex, production-level data systems in cloud environments.What is the Medallion Architecture in Databricks, and why is it important for data pipelines?The Medallion Architecture in Databricks organizes data into Bronze, Silver, and Gold layers, ensuring that raw data is progressively cleaned, validated, and enriched for analytics. Bronze stores raw ingestion, Silver provides curated and standardized datasets, and Gold delivers high-value analytical data ready for dashboards, reports, or machine learning. This architecture allows data engineers to build robust, scalable, and reliable pipelines, maintain data quality, and enable enterprise-level data governance using Delta Lake and Unity Catalog, making it essential for any modern data engineering workflow.Why would you want to take this course?Our answer is simple: The quality of teachingOAK 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' expertiseVideo and Audio Production QualityAll our content is created/produced as high-quality video/audio to provide you the best learning experienceYou will be,Seeing clearlyHearing clearlyMoving through the course without distractionsYou'll also get:Lifetime Access to The CourseFast & Friendly Support in the Q&A sectionUdemy Certificate of Completion Ready for DownloadWe offer full support, answering any questionsDive in now into the "Databricks | Spark ETL & Delta Lake Data Engineering Mastery" course.Learn Databricks from Spark ETL to Unity Catalog and Medallion pipelines to build scalable, high-impact data workflows
Who this course is for
Anyone who wants to learn data engineering through real, end-to-end Databricks workflows
Students, analysts, or professionals interested in Databricks, Apache Spark, or modern data platforms
Those seeking a hands-on guide to building ETL pipelines using the Lakehouse and Medallion (Bronze–Silver–Gold) Architecture
Anyone curious about how large-scale data systems work in real-world organizations
Learners who want to strengthen their Python and SQL skills through practical data engineering projects
Aspiring data engineers looking to gain industry-ready experience with Spark,Unity Catalog, and the Databricks ecosystem


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