In Databricks, a job cluster is a computing environment created to run a job. It’s not just a tool but a crucial part of your workflow. It manages resources and ensures that jobs have the necessary processing power, allowing you to focus on your tasks without worrying about infrastructure.

There are two main types of job clusters:

• New Job Cluster: This cluster is explicitly created for a single job run. It starts when the job begins and terminates once the job is completed. This is useful if you need a specific configuration or set of libraries for a particular job.
• Shared Job Cluster: This type of cluster can be used by multiple tasks within the same job. It’s created when the first task starts and terminates after the last task finishes. This is a more efficient way to utilize resources if you have multiple functions with similar requirements.

Key benefits of using job clusters:

• Resource isolation: Each job has dedicated resources, preventing interference from other jobs.
• Customizable configuration: You can tailor the cluster configuration to meet the specific needs of your job.
• Cost optimization: You can choose the correct cluster size for your job, avoiding overprovisioning and unnecessary costs.