Databricks Notebooks Explained — Your First Steps in Data Engineering
If you’re new to Databricks, chances are someone told you “Everything starts with a Notebook.”
They weren’t wrong.
In Databricks, a Notebook is where your entire data engineering workflow begins from reading raw data, transforming it, visualizing trends, and even deploying jobs. It’s your coding lab, dashboard, and documentation space all in one.
What Is a Databricks Notebook?
A Databricks Notebook is an interactive environment that supports multiple programming languages such as Python, SQL, R, and Scala.
Each Notebook is divided into cells you can write code, add text (Markdown), and visualize data directly within it.
Unlike local scripts, Notebooks in Databricks run on distributed Spark clusters. That means even your 100 GB dataset is processed within seconds using parallel computation.
So, Notebooks are more than just code editors they are collaborative data workspaces for building, testing, and documenting pipelines.
How Databricks Notebooks Work
Under the hood, every Notebook connects to a cluster a group of virtual machines managed by Databricks.
When you run code in a cell, it’s sent to Spark running on the cluster, processed there, and results are sent back to your Notebook.
This gives you the scalability of big data without worrying about servers or configurations.
Setting Up Your First Cluster
Before running a Notebook, you must create a cluster it’s like starting the engine of your car.
Here’s how:
Step-by-Step: Creating a Cluster in a Standard Databricks Workspace
- Navigate to Compute
- a. In the left-hand menu of your Databricks workspace, click on Compute.
- b. This opens the Compute dashboard, where you can view existing clusters or create a new one.
(Use your screenshot 1 here “Compute” page)
- Create a New Cluster
- a. Click Create Compute on the top right.
- b. Enter a name for your cluster for example, cf_dev_cluster or cf_unity_catalog.
- c. Choose a Policy (keep it “Unrestricted” if you’re testing).
- Select Databricks Runtime
- a. Pick a Databricks Runtime version (for example, 16.4 LTS which includes Apache Spark 3.5.2 and Scala 2.12).
- b. LTS (Long Term Support) versions are recommended for stability.
- Choose Node Type
- a. For dev or learning environments, select a small node like Standard_D4ds_v5 (4 cores, 16 GB memory).
- b. You can select Single Node if only you are using it for testing.
(Use your screenshot 2 here cluster configuration view)
- Set Auto-Termination
- a. To control cost, set “Terminate after 10 minutes of inactivity.”
- b. This ensures the cluster shuts down automatically when idle.
- Review & Create
- a. Review all settings and click Create Compute.
- b. Within a few minutes, the cluster will be up and running indicated by a green icon next to its name.
Once the cluster is active, you’ll see a green light next to its name now it’s ready to process your code.
Creating Your First Notebook
Now, let’s build your first Databricks Notebook:
- Go to Workspace → Create → Notebook.
- Name it Getting_Started_Notebook.
- Choose your default language Python or SQL.
- Click Create.
- At the top, select Attach to Cluster → choose your cluster.
Your Notebook is now live ready to connect to data and start executing.
Loading and Exploring Data
Let’s say you have a sales dataset in Azure Blob Storage or Data Lake. You can easily read it into Databricks using Spark:
| df = spark.read.csv(“/mnt/data/sales_data.csv”, header=True, inferSchema=True) display(df.limit(5)) |
Now, you can transform the data:
| from pyspark.sql.functions import col, sum summary = df.groupBy(“Region”).agg(sum(“Revenue”).alias(“Total_Revenue”)) display(summary) |
| %sql SELECT Region, SUM(Revenue) AS Total_Revenue FROM sales_data GROUP BY Region ORDER BY Total_Revenue DESC |
Databricks Notebooks include built-in charting tools.
After running your SQL query:
Click + → Visualization → choose Bar Chart.
Assign Region to the X-axis and Total_Revenue to the Y-axis.
Congratulations — you’ve just built your first mini-dashboard!
Real-World Example: ETL Pipeline in a Notebook
In many projects, Databricks Notebooks are used to build ETL pipelines:
- Extract data from source (Azure SQL, Blob, or API).
- Transform data using Spark and PySpark.
- Load the processed data into Delta Lake or SQL database.
Each stage is often written in a separate cell, making debugging and testing easier.
Once tested, you can schedule the Notebook as a Job running daily, weekly, or on demand.
Best Practices
- Keep each Notebook focused on one task (e.g., ingestion, cleaning, or aggregation).
- Use
%runcommand to call reusable Notebooks. - Save credentials securely in Databricks Secrets never hardcode keys.
- Always detach and terminate clusters when not in use to save costs.
- For long pipelines, break logic into multiple Notebooks linked by Jobs.
To conclude, Databricks Notebooks are not just a beginner’s playground they’re the backbone of real data engineering in the cloud.
They combine flexibility, scalability, and collaboration into a single workspace where ideas turn into production pipelines.
If you’re starting your data journey, learning Notebooks is the best first step.
They help you understand data movement, Spark transformations, and the Databricks workflow everything a data engineer need.
We hope you found this blog useful, and if you would like to discuss anything, you can reach out to us at transform@cloudFronts.com
