Get comfortable inside the Databricks workspace. This beginner tutorial walks through notebooks, compute, the file system, and simple habits that keep your work organized in Databricks Free Edition.
When you first log into Databricks, you're greeted by a left sidebar full of options. It can feel overwhelming, what's a Catalog? What's the difference between Workspace and Home? Why are there so many sections?
This chapter is your map. We'll walk through every sidebar item you see in Databricks Free Edition, explain what it does, whether you can use it hands-on or just understand it conceptually, and show you how it all connects.
The left sidebar organizes Databricks into logical sections. Think of it as the table of contents for everything you can do on the platform.
When you reference data in code, you use this full path:
For notebooks running PySpark code, Databricks automatically attaches serverless compute when you run your first cell. This eliminates the traditional "create a cluster and wait" step that older Databricks versions required.
The SQL section provides a complete analytics environment. Each component serves a specific purpose:
An interactive environment for writing and running SQL queries. Includes:
This is often the fastest way to explore data and test queries before putting them in notebooks.
Save your SQL queries for reuse. Saved queries can be:
Build visualizations from your SQL queries. The dashboard editor lets you:
Databricks offers two AI assistants that serve different purposes. Both are available in Free Edition.
Set up notifications when query results meet certain conditions. For example:
Alerts work with scheduled queries to monitor your data automatically.
View a log of all SQL queries executed in your workspace. Useful for:
The AI/ML section provides tools for machine learning workflows:
An interactive environment to test AI and LLM models with prompts. You can select from available models, adjust parameters like temperature and max tokens, and compare outputs. This is available in Free Edition and useful for experimenting with models before integrating them into notebooks or applications.
AI Gateway provides direct access to large language model endpoints from within Databricks. Free Edition gives you a curated set of foundation models from major providers and open-source families, callable directly from your notebooks without setting up your own API keys. The exact model list changes over time, so check the Playground or AI Gateway page in your workspace for what is available today.
Discover helps you search and find data assets across your workspace. It provides a unified search experience for tables, notebooks, dashboards, and other assets, making it easier to find relevant data and work created by your team.
This works in Free Edition! Chapter 21 covers MLflow experiment tracking in detail.
The Feature Store manages reusable ML features, pre-computed values used across multiple models.
The Model Registry stores trained ML models with versioning, staging, and deployment tracking.
Now that you've seen each component, let's visualize how data flows through the entire platform:
Here's a complete reference showing where each sidebar item is covered in this book:
Now that you understand the workspace, here's what to do first:
Before loading data, you'll create a Unity Catalog volume to store your files. This is the modern, recommended approach for file storage in Databricks:
This volume will store all the CSV files you download throughout the book. Files uploaded here are accessed using the path format:
In your new notebook, type this in the first cell and press Shift+Enter:
The first time you run code, you'll see "Connecting to compute..." for about 30-60 seconds. After that, subsequent cells run quickly.
Try querying the sample NYC taxi data:
If this works, you're ready to continue to the next chapter!
Create a folder structure for this book:
A key difference from older Databricks versions: Free Edition is fully serverless, so there is no cluster page to manage and nothing to start by hand. Here's what happens when you work:
You now have a complete map of Databricks Free Edition. You understand:
In the next chapter, we'll start learning data engineering fundamentals, the concepts and thinking patterns that apply everywhere, regardless of which specific tools you use.
You're ready. Let's build something real.