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Complete the Spark Universal connection configuration in the Spark configuration tab of the Run view of your Job. This configuration is effective on a per-Job basis.
Cloudera Data Engineering | Studio submit Jobs and collects the execution information of your
Job from Cloudera Data Engineering service. For more information, see Defining Cloudera Data Engineering connection parameters with Spark Universal. |
Databricks | Studio submits Jobs and collects the execution information of
your Job from Databricks. The Spark driver runs either on a
transient Databricks cluster or on an interactive Databricks cluster
on GCP, AWS, or Azure. For more information, see Defining Databricks connection parameters with Spark Universal. |
Dataproc | Studio submits Jobs and collects the execution information of
your Job from Dataproc. For more information, see Defining Dataproc connection parameters with Spark Universal. |
Kubernetes | Studio submits Jobs and collects the execution information of
your Job from Kubernetes. The Spark driver runs on the cluster
managed by Kubernetes and can run independently from your
Studio. For more information, see Defining Kubernetes connection parameters with Spark Universal. |
Local | Studio builds the Spark environment in itself at runtime to run
the Job locally within the Studio. With this mode, each processor of
the local machine is used as a Spark worker to perform the
computations. For more information, see Defining Local connection parameters with Spark Universal. |
Standalone | Studio connects to a Spark-enabled cluster to run the Job from
this cluster. For more information, see Defining Standalone connection parameters with Spark Universal. |
Yarn cluster | Studio submits Jobs and collects the execution information of
your Job from Yarn and ApplicationMaster. The Spark driver runs on
the cluster and can run independently from your Studio. For more information, see Defining Yarn cluster connection parameters with Spark Universal. |