Google Cloud Dataproc
Submit Spark/Hive jobs and manage Dataproc clusters (Google OAuth2).
OCNORA ships 12 typed Google Cloud Dataproc operations. Each node has a schema-driven form, input and output tables beside its settings, and is pre-flighted by Workflow Health before your first real run.
Before you connect Google Cloud Dataproc
- Authentication
- OAuth 2.0
- Credential
- {"client_id", "client_secret", "refresh_token"}
- Connection test
- Available: the stored credential can be checked against the provider when you add it. This is not a record that a test was run or passed.
What you can do with Google Cloud Dataproc
Operations, grouped by what they do to your data.
Create and send 3
Read and search 4
Update 2
Delete and cancel 3
A sample Google Cloud Dataproc workflow
- 1A core triggerStart the run from a chat, form, manual, schedule, webhook, workflow trigger — no Google Cloud Dataproc trigger is needed.
- 2Get Clusterdataproc.get_clusterGets the resource representation for a cluster in a project.
- 3Create Clusterdataproc.create_clusterCreates a cluster in a project.
An outline built from this provider’s node contracts, not a recorded run. Each step’s output fields are available to the next in the field picker.
Every node below has a reference page: what it does, the connection it needs, its config fields with types, constraints and examples, and the shapes it emits.
Operations 12
- Create ClusterCreates a cluster in a project.dataproc.create_cluster
- Delete ClusterDeletes a cluster in a project.dataproc.delete_cluster
- Delete JobDeletes the job from the project.dataproc.delete_job
- Get ClusterGets the resource representation for a cluster in a project.dataproc.get_cluster
- Get JobFetch a Dataproc job's state and driver output URI.dataproc.get_job
- List ClustersList Dataproc clusters in a region.dataproc.list_clusters
- List JobsLists regions/{region}/jobs in a project.dataproc.list_jobs
- Start ClusterStart a stopped Dataproc cluster (long-running operation).dataproc.start_cluster
- Stop ClusterStop a running Dataproc cluster (long-running operation).dataproc.stop_cluster
- Submit JobSubmit a PySpark, Spark SQL or Hive job to a Dataproc cluster.dataproc.submit_job
- Update ClusterUpdates a cluster in a project.dataproc.update_cluster
- Update JobUpdates a job in a project.dataproc.update_job
Google Cloud Dataproc questions
- How does OCNORA authenticate with Google Cloud Dataproc?
- With a stored OAuth 2.0 credential, shaped {"client_id", "client_secret", "refresh_token"}. A connection test can check it when you add it.
- What can I automate with Google Cloud Dataproc?
- 12 typed operations, each with a reference page listing its config fields and the shapes it emits.
- What if the Google Cloud Dataproc operation I need is not listed?
- Where Google Cloud Dataproc has an HTTP API, call it from the HTTP node with the same credential, or describe the step to the AI builder, which asks our team for a typed node when one is missing.
- Are there templates that use Google Cloud Dataproc?
- Not yet. Start from a blank workflow, or describe the workflow to Build with AI.
- How are Google Cloud Dataproc calls controlled?
- Every call and every run step is audited, runs count against the execution budget, and Workflow Health pre-flights the workflow when you save it.
See your first Google Cloud Dataproc workflow run.
Book a technical demo. We add the connection, pick a template or describe the workflow to Build with AI, and Workflow Health checks it before it runs.