Introduction

This section builds the mental model you’ll rely on for the rest of the guide: what data engineering on Databricks actually means, how Databricks relates to open-source Apache Spark, and how the platform is architected under the hood – control plane, compute plane, and the account/workspace/metastore hierarchy. It ends with getting a real workspace in your hands, either free or on AWS, so Section 4 onward is hands-on.

flowchart TD
    A[Apache Spark<br/>processing engine] --> B[+ Photon, Delta Lake,<br/>Unity Catalog, notebooks]
    B --> C[Databricks Platform]
    C --> D{Control plane<br/>Databricks-managed}
    C --> E{Compute plane<br/>your AWS account<br/>or serverless}
    D --> F[Workspace]
    E --> F

Lectures

#	Lecture	Est. Read
1	Introduction to Data Engineering	9 min read
2	Apache Spark to Data Engineering Platform	8 min read
3	Introduction to Databricks Platform	6 min read
4	Databricks Platform Architecture	13 min read
5	Databricks Platform Access - Paid vs Free	4 min read
6	Creating Databricks Free Account	4 min read

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Table of contents

• Introduction to Data Engineering
• Apache Spark to Data Engineering Platform
• Introduction to Databricks Platform
• Databricks Platform Architecture
• Databricks Platform Access - Paid vs Free
• Creating Databricks Free Account