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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 workflows

In 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 Databricks

Apply the Medallion Architecture (Bronze → Silver → Gold) confidently

Use Unity Catalog for secure and scalable data governance

Clean, transform, enrich, and analyze real-world datasets

Apply data quality checks, normalization, and advanced Spark operations

Work with notebook workflows and Databricks compute efficiently

Create analytical datasets ready for dashboards, BI tools, or machine learning

Develop the mindset and skills of a professional data engineer working with complex, production-level data systems

You 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 Matters

Databricks 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 data

The practical experience to build scalable pipelines

The confidence to operate in real-world cloud environments

Whether 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 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 "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

Databricks is one of the most in demand big data tools around. It is a fast, easy, and collaborative Spark based big data analytics service designed for data science, ML and data engineering workflows.

The course is packed with lectures, code-along videos and dedicated challenge sections. This should be more than enough to keep you engaged and learning! As an added bonus you will also have lifetime access to all the lectures… and I have provided detailed notebooks as a downloadable asset, the notebooks will contain step by step documentation with additional resources and links.

I have ensured that the delivery of the course is engaging and concise, the curriculum is extensive yet delivered in an efficient way. The course will provide you with hands-on training utilising a variety of different data sets.

The course is aimed at teaching you PySpark, Spark SQL in Python and the Databricks Lakehouse Architecture.

You will primarily be using Databricks on Microsoft Azure in addition to other services such as Azure Data Lake Storage Gen 2,  Azure Repos and Azure DevOps.

The course will cover a variety of areas including:

Set Up and Overview

Azure Databricks Notebooks

Spark SQL

Reading and Writing Data

Data Analysis and Transformation with Spark SQL in Python

Charts and Dashboards in Databricks Notebooks

Databricks Medallion Architecture

Accessing Data in Cloud Object Storage

Hive Metastore

Databases, Tables and Views in Databricks

Delta Lake / Databricks Lakehouse Architecture

Spark Structured Streaming

Delta Live Tables

Databricks Jobs

Access Control Lists (ACLs)

Databricks CLI

Source Control with Databricks Repos

CI/CD on Databricks