Azure Databricks is designed for big data analytics and machine learning. At the heart of Databricks service lies the Azure Databricks Cluster which is a set of computation resources and configurations on which you run data processing tasks, analytics, and machine learning workloads.
What is an Azure Databricks Cluster?
An Azure Databricks Cluster is a group of virtual machines configured and managed by Azure Databricks to execute Spark jobs. These clusters can be spun up and down automatically, based on the workload, ensuring that you only use resources when you need them. The primary components of a Databricks Cluster include:
- Driver Node: Manages the SparkContext and interprets the code sent to the cluster.
- Worker Nodes: Execute the code sent by the driver node, performing tasks such as data processing, transformations, and actions.
Clusters can be customized based on the size, type of virtual machines, and number of worker nodes, allowing them to scale to meet the demands of any workload, from small jobs to large-scale data processing.
Benefits of Azure databricks cluster
- Scalability: Azure Databricks clusters can automatically scale up and down based on the current workload. This flexibility ensures efficient resource usage and cost management, accommodating everything from small batch jobs to extensive data processing tasks without manual intervention.
- Performance: Built on Apache Spark, Azure Databricks clusters offer high performance for big data processing and analytics. The distributed nature of Spark allows it to process large volumes of data rapidly by dividing the workload across multiple nodes.
- Integration with Azure Services: Azure Databricks integrates seamlessly with other Azure services such as Azure Storage, Azure SQL Data Warehouse, and Azure Machine Learning. This integration simplifies data workflows and enhances the capability to build comprehensive analytics and machine learning solutions.
- Collaboration: Databricks notebooks provide an interactive workspace for developers, data scientists, and analysts. These notebooks support multiple languages (Scala, Python, R, SQL) and facilitate real-time collaboration, version control, and sharing of insights and visualizations.
- Cost Efficiency: With Azure Databricks' pay-as-you-go model, you only pay for the resources you use. The automatic scaling feature ensures that you do not overpay for idle resources, optimizing your expenditure on cloud infrastructure.
- Security and Compliance: Azure Databricks provides enterprise-level security with features like role-based access control, encryption at rest and in transit, and compliance with standards such as HIPAA, GDPR, and SOC 2. This ensures that your data and analytics workflows meet stringent security and compliance requirements.
- Ease of Use: The Azure Databricks platform abstracts much of the complexity involved in managing Spark clusters. It provides an intuitive interface for cluster configuration, job scheduling, and monitoring, reducing the operational burden on your data engineering team.
Creating Azure databricks cluster (Demo)
In the left menu of the Databricks workspace, select Compute and click on Create Compute.
![Azure Databricks]()
In the intermediate window, click on the pencil to provide a name for your cluster. In this article, a Demo Cluster is used.
In this article, the cluster policy is unrestricted with a single note
Provide the Databricks runtime version suitable for your workload.
The cluster set is designed to terminate after 10 minutes of inactivity to prevent incurring cost when not in use.
![Demp Cluster]()
In the Summary window, we can see the Unity Catalog and the Photon badges.
Click on Create Compute and in less than 5 minutes, the computer will be up and running as seen in the screenshot below.
![Create Compute]()
To stop the cluster from running, rectangle and click OK. To clone, delete or edit cluster permissions, click the ellipsis.
![OK]()