How to Run Your First Notebook in Databricks: From Beginner to Advanced

Table of Contents

🧠 How to Run Your First Notebook in Databricks: From Beginner to Advanced

Whether you’re a data analyst, engineer, scientist, or just a curious tech enthusiast, Databricks offers a powerful platform to explore big data and AI effortlessly using notebooks. In this guide, we’ll walk you through the complete journey — from setting up your environment to mastering advanced notebook workflows in Databricks.

🔰 Table of Contents

🔎 What is a Databricks Notebook?

A Databricks notebook is a collaborative, interactive environment where you can write code in Python, SQL, R, Scala, or Java, and view results immediately. It’s like Jupyter Notebook but powered by Apache Spark under the hood — perfect for data analytics and machine learning workflows.

✅ Step 1: Set Up Your Databricks Workspace

You can get started with either:

Databricks Community Edition (Free): https://community.cloud.databricks.com
Paid cloud workspaces on Azure or AWS

Create an Account:

⚙️ Step 2: Create a New Cluster

A cluster is a collection of computation resources (Spark jobs run here).

How to create:

Go to Compute tab
Click Create Cluster
Choose:
- Cluster name
- Runtime version (e.g., 14.3 LTS for latest features)
- Autoscaling & node type (select small for testing)

🔄 Wait for the cluster to be in Running state before attaching a notebook.

📓 Step 3: Create and Open a Notebook

Go to Workspace
Click Create → Notebook
Name your notebook (e.g., MyFirstNotebook)
Select Default Language: Python / SQL / Scala / R

✍️ Step 4: Write Your First Code (Beginner)

Try running a simple Python print statement:

print("Hello, Databricks!")

Or a simple SQL query:

%sql
SELECT "Hello from SQL" AS greeting;

🔗 Step 5: Attach Notebook to Cluster

At the top of the notebook, click Detached → select your running cluster
Now your code will execute on that cluster

▶️ Step 6: Execute Code Cells

Press Shift + Enter or click the Run icon on each cell
You can add multiple cells using the + sign
Check output immediately below the cell

🧙 Step 7: Use Magic Commands and Shortcuts

Databricks supports magic commands to switch between languages or shell access:

Command	Purpose
`%python`	Run cell as Python
`%sql`	Run cell as SQL
`%fs`	File system commands
`%sh`	Shell commands
`%md`	Markdown rendering

Example:

%fs ls /databricks-datasets

Useful Shortcuts:

Esc + A → Insert cell above
Esc + B → Insert cell below
Ctrl + / → Comment/Uncomment

📁 Step 8: Import Data into Your Notebook

Option 1: Upload file manually

Click Data tab → Add Data → Upload File
Access it using:

df = spark.read.csv("/FileStore/tables/mydata.csv", header=True)

Option 2: Use built-in datasets

df = spark.read.csv("/databricks-datasets/airlines/part-00000", header=True)

📊 Step 9: Visualize Data (Charts/Graphs)

Run a DataFrame cell
Click on the “+Visualization” icon
Choose from:
- Bar Chart
- Line Chart
- Pie Chart
- Scatter Plot
- Map

You can also use:

display(df.groupBy("Year").count())

⏰ Step 10: Schedule Notebooks with Jobs

Go to Workflows → Jobs
Click Create Job
Select:
- Notebook path
- Cluster
- Schedule (e.g., Daily at 8 AM)
Monitor job history from dashboard

This is great for ETL or automated reports.

🧬 Step 11: Version Control with Git

Integrate your notebook with GitHub or Azure DevOps.

Steps:

Click on Revision History
Link to a Git provider
Clone repo and commit directly from the notebook interface

💡 Step 12: Best Practices for Notebook Development

Practice	Description
Use markdown cells	Document your code clearly
Modularize code	Use functions or `%run` sub-notebooks
Avoid hardcoding	Use widgets or config files
Use checkpoints	Export versions or use Git
Cache dataframes	For repeated heavy queries
Clean up resources	Stop unused clusters

🧾 Final Thoughts

Databricks notebooks combine the power of big data with the simplicity of notebooks — making them a go-to tool for everyone from analysts to AI engineers. Once you’ve mastered your first notebook, explore Delta Lake, MLflow, and Unity Catalog to expand your skills even further.

📌 Bonus: Sample Notebook Flow

Step	Code	Description
Load CSV	`spark.read.csv()`	Load data from FileStore
Clean Data	`df.dropna()`	Remove nulls
Transform	`df.withColumn()`	Add calculated columns
Analyze	`df.groupBy().agg()`	Perform aggregation
Visualize	`display()`	Generate charts
Schedule	`Jobs`	Automate daily run

🚀 Ready to launch your first project?

Try building a notebook that:

Loads a dataset
Cleans & filters it
Displays visualizations
Runs on a schedule

Happy Notebooks! 🧪