How to read this path
Read top to bottom. Each article is a small step that answers one question, shows one real-world example, and prepares the reader for the next article.
What Is Databricks?
A plain-English definition of Databricks, what it bundles together, and why teams reach for it instead of stitching tools by hand.
Why Databricks Exists
The problem Databricks solves: tool sprawl, fragile ETL, duplicated data, and teams that cannot agree on one governed source of truth.
Databricks vs Spark
Spark is the engine; Databricks is the platform around it. This article makes that separation practical instead of theoretical.
Databricks vs Snowflake
A simple comparison of lakehouse-style engineering and warehouse-first analytics so you can tell when each platform is the better fit.
Lakehouse Explained
A beginner-friendly explanation of the lakehouse idea and why Databricks keeps using that term.
Workspace Basics
What the Databricks workspace is, what you click first, and how it organizes the work you are about to do.
Notebooks Basics
How Databricks notebooks work, why teams like them, and how they connect code, text, and results in one place.
Clusters vs SQL Warehouses
A clear guide to when you need Spark compute and when a SQL warehouse is the better tool for the job.
Delta Lake Basics
Why Delta Lake matters, what problem it solves on object storage, and why it is central to Databricks architecture.
Jobs and Workflows Basics
How Databricks runs repeatable work, schedules pipelines, and chains tasks into a dependable delivery flow.