Databricks Notebooks — Basics, Cells & Commands

Welcome back to ShopWave, our fictional retail company.
You’ve logged into Databricks, opened your cluster, and now it’s time to write your first notebook.

Your senior engineer says:

“Think of a notebook as your lab notebook — each cell is a step in your experiment.”

Let’s walk through it step by step.

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🖥️ What Is a Databricks Notebook?

A Databricks notebook is an interactive web-based tool where you can:

• Write code in Python, SQL, R, or Scala
• Run commands interactively
• Document your workflow with Markdown
• Visualize data and charts
• Collaborate with teammates in real-time

It’s the central workspace for data engineers, analysts, and data scientists.

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🧩 Notebook Structure — Cells, Commands & Types

A notebook is made up of cells. Each cell can contain:

1️⃣ Code Cells

• Run your programming code (Python, SQL, Scala, R)
• Execute transformations, queries, or ML training
• Example:

# Python code cell
sales = spark.read.table("sales")
sales.show(5)

2️⃣ SQL Cells

• Run queries directly against Delta tables
• Can also visualize data using built-in charting

-- SQL code cell
SELECT product_id, SUM(quantity) AS total_sold
FROM sales
GROUP BY product_id
ORDER BY total_sold DESC
LIMIT 10;

3️⃣ Markdown / Text Cells

• Add notes, explanations, and documentation
• Supports headings, lists, links, and images

# Sales Analysis
This cell explains the top-selling products in Q3 2025.

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🔥 Running Cells

• Press Shift + Enter → runs the current cell and moves to the next
• Press Ctrl + Enter → runs the current cell only
• Press Alt + Enter → runs the current cell and inserts a new one below

Your ShopWave workflow:

1. Engineer loads raw sales data in a Python cell
2. Analyst runs a SQL query cell to summarize data
3. Team adds Markdown notes for context
4. Visualize results in the same notebook

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🧠 Magic Commands & Shortcuts

Databricks also has magic commands, which make notebooks super flexible:

Command	Use Case
%sql	Run SQL in a Python or Scala notebook
%python	Switch back to Python if in SQL notebook
%md	Render Markdown text
%run ./notebook_path	Import and run another notebook
%fs	Work with Databricks File System (DBFS)

Shortcuts make you faster and improve collaboration.

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🤝 Collaboration Features

Notebooks aren’t just for solo work—they are team-friendly:

• Real-time editing (like Google Docs)
• Comment on cells for discussion
• Version history to revert changes
• Git integration to track notebook changes

ShopWave’s ML team uses this to experiment with models, then merge the notebook into production workflows seamlessly.

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📊 Visualizations in Notebooks

You can create:

• Bar charts
• Line charts
• Pie charts
• Scatter plots

Directly from SQL queries or DataFrames.
For ShopWave, visualizing top-selling products by region is one click away.

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🏁 Quick Summary

• Databricks notebooks are interactive coding and documentation tools.
• Comprised of cells: Code (Python/SQL/Scala/R), Markdown, and Visualizations.
• Magic commands enhance functionality.
• Collaboration is seamless with real-time editing, comments, version control, and Git integration.
• Notebooks are essential for data engineering, ML, analytics, and dashboard prep.

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🚀 Coming Next

👉 Databricks Security Basics — Tokens, Users & Groups