developer-tools · analytics · 0 triggers · 200 actions
Databricks is the lakehouse company, helping data teams solve the world's toughest problems with unified analytics platform for big data and AI. Add Databricks to your Stream Deck workflows with guided setup for the connection details it needs.
Add Databricks to a workflow and pair it with a Stream Deck button to run it in one press — handy for developer-tools tasks. Combine it with OBS, your Elgato tools, or any other app you connect to build a routine that fits how you work.
Browse all templatesSteps your workflow can perform in Databricks.
Tool to cancel a Databricks job run asynchronously. Use when you need to terminate a running job. The run will be terminated shortly after the request completes. If the run is already in a terminal state, this is a no-op.
Tool to create a new Databricks Spark cluster with specified configuration. Use when you need to provision compute resources for data processing. This is an asynchronous operation that returns a cluster_id immediately with the cluster in PENDING state. The cluster transitions through states until reaching RUNNING.
Creates a new Databricks job with specified tasks, schedules, and notifications. Use this action when you need to define a new workflow or data pipeline in Databricks. Each job must have at least one task, and each task requires a task_key, a task type (notebook_task, spark_jar_task, etc.), and a cluster specification (existing_cluster_id, new_cluster, or job_cluster_key). Use the format='MULTI_TASK' parameter for jobs with multiple tasks that have dependencies. The created job will not run automatically unless a schedule is specified.
Tool to create a new group in Databricks workspace using SCIM v2 protocol. Use when you need to create a new security group with a unique display name, optionally with initial members, entitlements, and roles.
Tool to create a new user in Databricks workspace using SCIM v2 protocol. Use when you need to provision a new user account with a unique userName (email), optionally with display name, activation status, group memberships, entitlements, and roles.
Tool to create a new MLflow experiment for tracking machine learning runs and models. Use when you need to organize and track ML experiments within Databricks. Returns RESOURCE_ALREADY_EXISTS error if an experiment with the same name already exists.
Tool to create a new MLflow run within an experiment for tracking machine learning execution. Use when starting a new ML training run, experiment execution, or data pipeline that needs parameter and metric tracking. Returns the created run with a unique run_id for subsequent metric and parameter logging.
Tool to create a provisioned throughput serving endpoint for AI models in Databricks. Use when you need to provision model units for production GenAI applications with guaranteed throughput. The endpoint name must be unique across the workspace and can consist of alphanumeric characters, dashes, and underscores. Returns a long-running operation that completes when the endpoint is ready.
Databricks is the lakehouse company, helping data teams solve the world's toughest problems with unified analytics platform for big data and AI.
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Follow the setup prompts for Databricks. Conductor Deck handles the app authorization or required connection details inside the workflow builder.
Add a Databricks action to your visual workflow canvas. Configure the options you need, then connect it to other steps.
Sync your workflow to any Stream Deck button. Press once to trigger the entire sequence.
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