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Types of Clusters in Databricks Made Easy
A Databricks cluster (also called compute) is a group of virtual machines that runs your notebooks, jobs and queries with Apache Spark. Choosing the right type affects speed, cost and how well teams can share resources.
This guide explains the main types of Databricks compute — all-purpose, job, single-node and multi-node, auto-scaling, SQL warehouses, serverless and GPU — plus access modes and how to pick the right one.
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Table of Contents
Overview of Databricks Clusters
Every Databricks cluster has a driver node (coordinates the work) and, for multi-node clusters, worker nodes (do the processing in parallel). You choose the VM size, the Databricks Runtime version, the number of workers and settings such as auto-termination.
What Are Clusters in Databricks?
- What they are: groups of VMs that work together to process data.
- What they run: data engineering jobs, data science notebooks, SQL queries and machine learning.
- How you pay: for Databricks Units (DBUs) plus the Azure VMs while the cluster is running.
Main Types of Databricks Compute
- All-purpose compute – interactive clusters for notebooks and development, shared by users.
- Job compute – created for a scheduled job and shut down when it finishes; cheaper for production pipelines.
- SQL warehouses – optimised for SQL queries and BI tools such as Power BI.
- Serverless compute – Databricks manages the infrastructure and starts it quickly; available for notebooks, jobs and SQL in supported regions.
- Instance pools – keep ready VMs to reduce cluster start time.
Single-Node Clusters
A single-node cluster runs everything on one machine (the driver), with no workers.
- Good for: learning, small datasets, testing code and lightweight ML.
- Pros: low cost, simple.
- Limits: not suitable for large data or heavy parallel processing.
Multi-Node Clusters
A multi-node cluster has one driver and several workers that process data in parallel.
- Good for: large-scale ETL, big data analytics and distributed ML.
- Pros: handles big workloads quickly.
- Tip: choose VM sizes based on whether your work needs more memory or more CPU.
Auto-Scaling Clusters
Auto-scaling clusters add or remove workers automatically between a minimum and maximum you set.
- Good for: workloads that change in size during the day.
- Pros: better performance at peaks and lower cost when quiet.
- Tip: combine with auto-termination so idle clusters shut down.
Access Modes for Shared Teams (Formerly High Concurrency)
Older Databricks versions had “High Concurrency” clusters for sharing between many users. Today, sharing and security are controlled by access modes:
- Standard (formerly Shared) – many users share one cluster with isolation between them; works with Unity Catalog.
- Dedicated (formerly Single user) – assigned to one user or group; needed for some workloads such as certain ML libraries.
For many-user SQL and BI workloads, SQL warehouses are usually the best choice.
GPU-Enabled Clusters
GPU clusters add graphics processors to the VMs for highly parallel maths.
- Good for: deep learning, computer vision, NLP and large ML training.
- Use with: Databricks Runtime for Machine Learning (GPU).
- Note: GPUs are expensive — use them only for workloads that benefit.
Which Cluster Type Fits Which Role?
- Data engineers: job compute for scheduled pipelines; all-purpose or serverless for development.
- Data analysts: SQL warehouses for queries and dashboards.
- Data scientists: single-node or multi-node ML runtime clusters; GPU clusters for deep learning.
- Teams sharing notebooks: all-purpose compute in Standard access mode.
Example Scenarios
- Development and testing: a developer uses a small single-node cluster to test PySpark code cheaply before running it on full data.
- Nightly ETL: a data team runs its daily pipelines on job compute with auto-scaling, so each run gets the resources it needs and the cluster shuts down afterwards.
- Image recognition: an ML team trains a deep learning model on a GPU cluster to cut training time.
(These are illustrative examples.)
Conclusion
Databricks offers different compute types for different jobs. Use job compute and auto-scaling for production pipelines, SQL warehouses for BI, single-node clusters for learning and small tests, Standard access mode for shared work, and GPUs only for deep learning. Always turn on auto-termination to control cost. Learn Databricks hands-on in our Azure Data Engineer training.
Frequently Asked Questions About Databricks Clusters
What types of compute does Databricks offer?
All-purpose compute, job compute, SQL warehouses, serverless compute and instance pools. Clusters can be single-node or multi-node, with optional auto-scaling and GPUs.
What is the difference between all-purpose and job compute?
All-purpose clusters are for interactive work and can be shared. Job compute is created for a job run and ends when the job finishes, which is usually cheaper.
Are High Concurrency clusters still used?
They are a legacy option. Today you use access modes (Standard or Dedicated) and SQL warehouses for many concurrent users.
How can I reduce Databricks cluster costs?
Use job compute for pipelines, enable auto-scaling and auto-termination, right-size VMs and use pools or serverless where suitable.
When should I use a GPU cluster?
For deep learning and other highly parallel ML workloads, not for normal ETL.
