Databricks Fundamentals

Core Databricks and Apache Spark concepts for a data engineer coming from a legacy warehouse or ETL background: workspace, Delta Lake, Unity Catalog, Medallion architecture, and the Lakeflow ingestion, transformation, and orchestration pillars.

If you’re coming from a legacy warehouse or a hand-built ETL tool, this part is where the Databricks-native vocabulary gets mapped onto what you already know: a Delta table instead of a heap table with a transaction log bolted on by convention, Unity Catalog instead of GRANT statements scattered across a dozen schemas, and the three Lakeflow pillars (Connect, Declarative Pipelines, Jobs) instead of a Talend job, a stored procedure, and a cron entry stitched together by hand. By Section 10 you’ll have a working mental model of the whole platform – ready to build something real in Part 2.

Sections

#	Section	Items
1	Before you start	3
2	Introduction	6
3	Sign up for Databricks Platform in AWS	8
4	Working in Databricks Workspace	8
5	Delta Lake - Deep Dive	12
6	Unity Catalog - Governance from day one	9
7	Medallion Architecture - Design and Implementation	6
8	Lakeflow Connect - Ingestion Pillar	8
9	Lakeflow Spark Declarative Pipelines - Transformation Pillar	8
10	Lakeflow Jobs - The Orchestration Pillar	8

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Table of contents

• Before you start
• Introduction
• Sign up for Databricks Platform in AWS
• Working in Databricks Workspace
• Delta Lake - Deep Dive
• Unity Catalog - Governance from day one
• Medallion Architecture - Design and Implementation
• Lakeflow Connect - Ingestion Pillar
• Lakeflow Spark Declarative Pipelines - Transformation Pillar
• Lakeflow Jobs - The Orchestration Pillar