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DuckyHelper in Databricks

DuckyHelper sees Databricks on your screen and, in early access, works in it with its own cursor to run SQL queries, manage clusters and jobs, browse Unity Catalog tables, track MLflow experiments, and build dashboards. It's built to ask before it sends or pays, and explains or draws on Databricks when you want to learn it.

Try asking
  • “List the catalog tables and publish a lakeview dashboard”
  • “Create a cluster in Databricks”
  • “Show me how to create a cluster”

Press fn (Ctrl+Shift+Space on Windows and Linux) and ask out loud. Doing the work for you is early access on Mac.

Download for freeSee how it works
Tables in DatabricksDone in 7sExample
List the catalog tables and publish a lakeview dashboard
  1. Listed the catalog tables in Databricks
  2. Created a lakeview dashboard
  3. Publish a lakeview dashboard

It's ready in Databricks. Nothing goes out until you press Send.

Databricks
7s · 1 app
DatabricksPublish a lakeview dashboardNeeds your OK
ToDana Lee (example)

Drawn from Databricks's own actions · early access on Mac

WHAT TO ASK

Things you can ask it to do in Databricks

Each one is something Databricks itself lets you do, said the way you'd ask. Questions about your screen work today; having it do the job is early access on Mac.

Ask about it

Works today

It sees your screen, answers out loud and draws on it.

  • “Explain this cluster to me”
  • “Where do I find the dashboards in Databricks?”
  • “Show me how to create a cluster”
  • “What does this button do?”
  • “Walk me through this screen”

Read and find

Early access

It opens Databricks and looks things up for you.

  • List the catalog tables
  • List the clusters
  • List the unity catalogs
  • Search the mlflow experiments
  • Search the mlflow runs
  • List the SQL warehouses
  • Look up an ML experiment
  • Look up the catalog details
Show all 138
  • Look up the SQL alert details
  • Look up the SQL query details
  • Look up an ML experiment by name
  • Look up the catalog table details
  • Look up the SQL warehouse details
  • Look up the catalog volume details
  • Look up a published lakeview dashboard
  • Look up a provider analytics dashboard
  • Look up the lakeview dashboard details
  • Look up a legacy SQL query
  • List the SQL query history
  • List the repos
  • List the delta live tables pipelines
  • Search the logged models
  • List the genie spaces
  • List the compute cluster node types
  • List the instance pools
  • Look up a mlflow run
  • List the compute cluster availability zones
  • Look up the app details
  • Look up the share details
  • List the genie conversation messages
  • List the genie conversations
  • Look up the metastore details
  • Look up the genie space details
  • Look up the cluster information
  • Look up the instance pool details
  • Look up the catalog grants
  • Read the DBFS file contents
  • Look up the external location details
  • List all jobs
  • List the DBFS directory contents
  • List the pipeline updates
  • List the job runs
  • List the users
  • Read a genie message
  • List the quality monitor refreshes
  • Check the table exists
  • Look up the access request destinations
  • Look up a catalog connection
  • Find a database instance by UID
  • Look up the marketplace consumer listings
  • Look up an ML feature tag
  • Look up the marketplace consumer providers
  • List the workspace groups
  • List the catalog schemas
  • List a workspace directory
  • Look up a logged model
  • Look up a genie message query result
  • Look up a lakeview dashboard schedule
  • Look up a notification destination
  • Look up a registered model
  • Look up a sharing provider
  • Look up the job details
  • Look up an SQL statement result chunk
  • List the compute cluster spark versions
  • Look up a database instance
  • List the members of a security group
  • Look up a global init script
  • Look up a workspace access detail
  • Look up the job permission levels
  • Look up a marketplace consumer listing
  • Look up a marketplace provider listing
  • Look up a marketplace consumer provider
  • Look up the current user information
  • Look up the ML experiment permissions
  • Look up the SQL warehouse permissions
  • Look up a sharing recipient
  • Look up the marketplace consumer personalization requests
  • Look up the effective catalog permissions
  • Look up the ML experiment permission levels
  • Look up the SQL warehouse permission levels
  • Look up an entity tag assignment
  • Look up the compute cluster permission levels
  • Look up a quality monitor
  • Look up an IP access list
  • Look up the compute cluster policy permissions
  • Look up a current metastore assignment
  • Look up the SQL statement status
  • Look up a job run
  • Look up a clean room asset
  • Look up a genie message attachment query result
  • Look up a metastore summary
  • Look up a user
  • Look up the app permissions
  • Look up the IAM permissions
  • Look up the share permissions
  • Look up a compute cluster policy
  • Look up the pipeline permissions
  • Look up the app permission levels
  • Look up the IAM permission levels
  • Look up the instance pool permissions
  • Export a workspace object
  • Look up the pipeline permission levels
  • Look up an SQL results download setting
  • Look up the compute cluster policy permission levels
  • Look up the instance pool permission levels
  • Look up an automatic cluster update setting
  • Look up a dashboard email subscriptions setting
  • Look up the DBFS file status
  • Look up the app update status
  • Look up a catalog schema
  • Look up a tag policy
  • List a job compliance for a policy
  • Search a vector search index
  • Look up a job policy compliance
  • Look up the workspace repo permission levels
  • Look up the ML model registry permission levels
  • Look up a cluster policy compliance
  • Look up a default namespace setting
  • Look up a disable legacy DBFS setting
  • Look up an ai/bi dashboard embedding access policy
  • Look up an enable notebook table clipboard setting
  • Look up the resource quota information
  • Look up a default warehouse ID setting
  • Look up a disable legacy access setting
  • Look up the ai/bi dashboard embedding approved domains
  • Look up the workspace object status
  • Look up all library statuses
  • Look up an enable results downloading setting
  • Look up an enhanced security monitoring setting
  • Look up the compute policy families
  • Look up a model version
  • Look up a catalog artifact allowlist
  • Look up a public workspace setting
  • Look up an enable export notebook setting
  • Look up a restrict workspace admins setting
  • Look up a latest provider analytics dashboard version
  • Look up a compliance security profile setting
  • Look up a workspace warehouse config

Write and send

Early access

It's built to ask before anything is sent or paid.

  • Create a cluster
  • Create an ML experiment
  • Create a lakeview dashboard
  • Create an SQL query
  • Create an ML forecasting experiment
  • Create a provider analytics dashboard
  • Create a legacy SQL query
  • Run a message attachment query
Show all 95
  • Update an ML experiment
  • Update an SQL dashboard
  • Update a compute cluster
  • Edit a cluster
  • Update a lakeview dashboard
  • Create a mlflow experiment run
  • Update an SQL query
  • Pin a compute cluster
  • Start a compute cluster
  • Restore an ML experiment
  • Publish a lakeview dashboard
  • Create an SQL alert
  • Update a catalog table
  • Create a catalog connection
  • Update an ML experiment run
  • Set a mlflow run tag
  • Set an ML experiment tag
  • Create a metastore
  • Restore an ML experiment run
  • Create an SQL query visualization
  • Create a logged model
  • Create an app
  • Restore the ML experiment runs
  • Create a notification destination
  • Create a sharing provider
  • Update an SQL alert
  • Create a genie space
  • Add a lakebase database as catalog
  • Create a genie message
  • Create a share
  • Create a database instance
  • Update a catalog connection
  • Create a global init script
  • Create a marketplace provider listing
  • Create a compute instance pool
  • Edit an SQL warehouse
  • Create a sharing recipient
  • Start an SQL warehouse
  • Create an external location
  • Create an IP access list
  • Submit an one-time run
  • Create a clean room
  • Create a job
  • Create a data quality monitor
  • Set the logged model tags
  • Update a metastore
  • Create a workspace repo
  • Update an SQL query visualization
  • Update a catalog function
  • Create a DBFS file stream
  • Update an app
  • Add a block to DBFS stream
  • Create access requests
  • Set or update an ML feature tag
  • Create a workspace directory
  • Update a notification destination
  • Update a sharing provider
  • Send the genie message feedback
  • Update a genie space
  • Run an SQL statement
  • Update a share
  • Start an app
  • Create a clean room auto-approval rule
  • Add a compute instance profile
  • Create a compute cluster policy
  • Update a global init script
  • Update an enable notebook table clipboard
  • Update the catalog grants
  • Update an external location
  • Update a metastore assignment
  • Edit a compute instance pool
  • Move a DBFS file or directory
  • Create an ML feature store online store
  • Update a job
  • Start a genie conversation
  • Create a tag policy
  • Update an enable results downloading
  • Update an enhanced security monitoring
  • Import a workspace object
  • Edit a compute instance profile
  • Update an enable export notebook
  • Create a marketplace consumer installation
  • Update a model version
  • Enable a system schema
  • Save a data vector index
  • Update a marketplace consumer installation
  • Update the external metadata

Manage

Early access

It tidies up and changes things in Databricks.

  • Delete a cluster
  • Unpin a compute cluster
  • Delete an ML experiment
  • Delete an SQL dashboard
  • Delete a compute cluster
  • Trash a lakeview dashboard
  • Delete an SQL query
  • Delete a catalog table
Show all 97
  • Delete a synced database table
  • Delete a catalog
  • Delete an ML experiment run
  • Delete the ML experiment runs
  • Delete an ML experiment run tag
  • Delete an SQL alert
  • Delete an SQL warehouse
  • Delete a legacy SQL alert
  • Delete a catalog connection
  • Delete an ML feature tag
  • Delete a logged model tag
  • Delete a metastore
  • Delete a logged model
  • Delete an app
  • Delete a lakeview dashboard schedule
  • Delete a notification destination
  • Trash a genie space
  • Cancel an SQL statement execution
  • Delete a share
  • Delete a listing from an exchange
  • Delete a database instance
  • Delete a global init script
  • Delete a compute instance pool
  • Delete a DBFS file or directory
  • Delete a pipeline
  • Update the access request destinations
  • Delete a sharing recipient
  • Delete a genie conversation message
  • Delete an external location
  • Delete a genie conversation
  • Update a workspace repo
  • Delete a workspace repo
  • Add a member to a security group
  • Cancel a job run
  • Delete a job run
  • Assign a metastore to a workspace
  • Delete a custom LLM agent
  • Cancel all job runs
  • Unassign a metastore from a workspace
  • Set the job permissions
  • Delete a workspace object
  • Update an SQL results download setting
  • Update the catalog workspace bindings
  • Edit a compute cluster policy
  • Update an automatic cluster update setting
  • Delete a compute cluster policy
  • Remove a compute instance profile
  • Set the ML experiment permissions
  • Set the SQL warehouse permissions
  • Update a dashboard email subscriptions setting
  • Delete an SQL results download setting
  • Set the compute cluster policy permissions
  • Update a tag policy
  • Delete a dashboard email subscriptions setting
  • Delete an ML feature store online store
  • Update the ML experiment permissions
  • Update the SQL warehouse permissions
  • Update the cluster policy permissions
  • Delete a tag policy
  • Update a default namespace setting
  • Set the app permissions
  • Set the IAM permissions
  • Update a disable legacy DBFS setting
  • Update an ai/bi dashboard embedding access policy
  • Update a default warehouse ID setting
  • Update a disable legacy access setting
  • Delete a default namespace setting
  • Delete a disable legacy DBFS setting
  • Delete an ai/bi dashboard embedding access policy
  • Update an IAM account access control rule set
  • Delete a default warehouse ID setting
  • Delete a disable legacy access setting
  • Update the app permissions
  • Update the IAM permissions
  • Set the compute instance pool permissions
  • Update the pipeline permissions
  • Update the instance pool permissions
  • Disable a system schema
  • Update a restrict workspace admins setting
  • Set the workspace repo permissions
  • Delete a vector search index
  • Delete a restrict workspace admins setting
  • Update the workspace repo permissions
  • Delete the ai/bi dashboard embedding approved domains
  • Delete a marketplace consumer installation
  • Set the workspace configuration status
  • Update the ai/bi dashboard embedding approved domains
  • Set a workspace warehouse config
  • Delete an ML feature engineering kafka config
BUILT TO ASK FIRST

It's built to wait for your OK in Databricks.

When a job ends in “Publish a lakeview dashboard”, it does the work, and is built to stop with it ready. You see exactly what it wrote and where it goes, then Send, Edit or Deny.

It works in Databricks signed in as you. If it hits a sign-in, you sign in yourself: it's built not to type your passwords or card numbers.

DatabricksPublish a lakeview dashboardNeeds your OK
ToDana Lee (example)

Example draft · early access on Mac

Why Use DuckyHelper?

Works in Databricks like you do

  • Early access: it sees Databricks on your screen and works in it with its own cursor, while you keep using yours
  • Nothing to set up: it works in Databricks signed in as you
  • Finishes multi-step jobs across Databricks and your other apps

Built to ask before it sends or pays

  • Built to ask first before it sends a message or pays for anything
  • Built not to type your passwords or card numbers for you
  • It listens and looks only while the pill is open

Shows you, not just tells you

  • Explains Databricks out loud and draws right on your screen
  • Works it out with you on a blackboard when you want to learn it

Always there, in every app

  • Press one key in any app and ask out loud: fn on a Mac, Ctrl+Shift+Space on Windows and Linux
  • Runs jobs on a schedule (early access)
  • For Mac, Windows and Linux

Want it in every app you use?

One account works in every app on your computer. Try it for 10 minutes, no card, then pick a plan.

SEE PRICING
FAQ

Frequently asked questions

On your screen, the way a person at your computer would. Press fn (Ctrl+Shift+Space on Windows and Linux) and ask about what you see in Databricks: it answers out loud and can draw on Databricks to show you where things are. In early access on Mac it also works in Databricks with its own cursor, signed in as you, while you keep using yours.

For example: “List the catalog tables and publish a lakeview dashboard” and “Create a cluster in Databricks”. Or ask about what you see: “Show me how to create a cluster”. Doing the work for you is in early access on Mac.

No. Open Databricks and sign in yourself, the way you always do. DuckyHelper works in the window you have open, signed in as you. It's built not to type your passwords or card numbers for you.

It's built to ask first before it sends a message or pays for anything, and to wait for your yes. It may not recognize every send or payment, so keep an eye on tasks that involve messages or money.

In Databricks, a job that ends in “Publish a lakeview dashboard” is built to stop there for your OK: Send, Edit or Deny.

Yes. Press one key and ask out loud. It answers out loud while you keep working, draws on your screen to show you where things are, or works it out with you on a blackboard.

Yes. DuckyHelper works in any app on your computer, so one job can move between Databricks and the other apps and pages you use.

Doing the work for you is new and in early access on Mac. Asking out loud, drawing on your screen and the blackboard work today on Mac, Windows and Linux.

No. It listens and looks only while the pill is open. When the pill is closed, nothing is heard or seen.

No, there is no charge per app. Try DuckyHelper for 10 minutes with no card. Plus is $20 a month, $14 a month for your first 3 months. See pricing.

Mac (Apple silicon, macOS 14 or later), Windows and Linux. Download DuckyHelper.

No. DuckyHelper is made by MingLLM, Inc. and is not affiliated with or endorsed by Databricks. It works in Databricks the same way you do, on your screen.

Try it in Databricks.Press one key and ask.

It sees Databricks, explains it out loud and does the work with you.Try it for 10 minutes, no card.

Download DuckyHelper
Questions and jobs

Do anything

Ask it a question or hand it a job, in any app.

Doing jobs for you is in early access on Mac. Asking works on Mac, Windows and Linux.