What Are Databricks Clusters? A Simple Guide for Beginners
A Databricks Cluster is a group of virtual machines (VMs) in the cloud that work together to process data using Apache Spark.
It provides the memory, CPU, and compute power required to run your code efficiently.
Clusters are used for:
- a. Running interactive notebooks
- b. Executing ETL and ELT pipelines
- c. Performing machine learning experiments
- d. Querying and transforming large datasets
Each cluster has two main parts:
- 1. Driver Node: Coordinates all tasks and collects results.
- 2. Executor Nodes: Perform the actual data computation in parallel.
Types of Clusters
Databricks supports multiple cluster types, depending on how you want to work.
| Cluster Type | Use Case |
| Interactive (All-Purpose) Clusters | Used for notebooks, ad-hoc queries, and development. Multiple users can attach their notebooks. |
| Job Clusters | Created automatically for scheduled jobs or production pipelines. Deleted after job completion. |
| Single Node Clusters | Used for small data exploration or lightweight development. No executors, only one driver node. |
How Databricks Clusters Work
When you execute a notebook cell, Databricks sends your code to the cluster.
The cluster’s driver node divides your task into smaller jobs and distributes them to the executors.
The executors process the data in parallel and send the results back to the driver.
This distributed processing is what makes Databricks fast and scalable for handling massive datasets.
Step-by-Step: Creating Your First Cluster
Let’s create a cluster in your Databricks workspace.
Step 1: Navigate to Compute
In the Databricks sidebar, click Compute. You’ll see a list of existing clusters or an option to create a new one.
Step 2: Create a New Cluster
Click Create Compute in the top-right corner.
Step 3: Configure Basic Settings
- a. Cluster Name: Give it a meaningful name like
data-engineering-dev-cluster. - b. Policy: Choose “Unrestricted” for testing or a company policy if enforced.
- c. Databricks Runtime: Select the latest Long-Term Support (LTS) version (for example, 16.4 LTS).
Step 4: Select Node Type
Choose the VM type based on your workload. For development, Standard_DS3_v2 or Standard_D4ds_v5 are cost-effective.
Step 5: Auto-Termination
Set the cluster to terminate after 10 or 20 minutes of inactivity. This prevents unnecessary cost when the cluster is idle.
Step 6: Review and Create
Click Create Compute. After a few minutes, your cluster will turn green, indicating it is ready to run code.
Clusters in Unity Catalog-Enabled Workspaces
If Unity Catalog is enabled in your workspace, there are a few additional configurations to note.
| Feature | Standard Workspace | Unity Catalog Workspace |
| Access Mode | Default is Single User. | Must choose Shared, Single User, or No Isolation Shared. |
| Data Access | Managed by workspace permissions. | Controlled through Catalog, Schema, and Table permissions. |
| Data Hierarchy | Database → Table | Catalog → Schema → Table |
| Example Query | SELECT * FROM sales.customers; | SELECT * FROM main.sales.customers; |
When you create a cluster with Unity Catalog, you will see a new Access Mode field in the configuration page. Choose “Shared” if multiple users need to access governed data under Unity Catalog.
Managing Cluster Performance and Cost
Clusters can become expensive if not managed properly. Follow these tips to optimize performance and cost:
a. Use Auto-Termination to shut down idle clusters automatically.
b. Choose the right VM size for your workload. Avoid oversizing.
c. Use Job Clusters for production pipelines since they start and stop automatically.
d. Leverage Autoscaling so Databricks can adjust the number of workers dynamically.
e. Monitor with Ganglia metrics to identify performance bottlenecks.
Common Cluster Issues and Fixes
| Issue | Cause | Fix |
| Cluster stuck starting | VM quota exceeded or region issue | Change VM size or region. |
| Slow performance | Too few workers or data skew | Increase worker count or repartition data. |
| Access denied to data | Missing storage credentials | Use Databricks Secrets or Unity Catalog permissions. |
| High cost | Idle clusters running | Enable auto-termination. |
Best Practices for Using Databricks Clusters
1. Always attach your notebook to the correct cluster before running it.
2. Use development, staging, and production clusters separately.
3. Keep the cluster runtime version consistent across environments.
4. Terminate unused clusters to reduce cost.
5. If you use Unity Catalog, prefer Shared clusters for collaboration.
To conclude, clusters are the heart of Databricks.
They provide the compute power needed to process large-scale data efficiently. Without them, Databricks Notebooks and Jobs cannot run. Once you understand how clusters work, you will find it easier to manage costs, optimize performance, and build reliable data pipelines.
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
