Inspect a Dataset Before Querying
Inspect dataset [dataset] in project [project]. Explain which tables and columns can answer [question].
google_bigquery
Google BigQuery AI Employee
Leina connects to Google BigQuery to help you inspect datasets and answer questions with SQL. Ask in your team chat. Leina does the work and brings back the result.
Connect Google BigQuery, choose what to share, and hand off your first task.
Leina + Google BigQuery Example Result
You
In [project] and [region], use [orders table] to compare completed orders by sales channel last month. Do not change any data.
Leina
I queried completed orders for the requested month and grouped the results by channel.
BigQuery · Channel split
Completed orders
The same month and order definition across both channels.
- Online store
- 180 completed orders.
- Marketplace
- 120 completed orders.
What Is a Google BigQuery AI Agent?
A BigQuery AI agent can inspect your tables, run a query for a defined business question, and summarize the completed results.
You specify the source and the destination. Leina uses the content available to your connection and returns the result for you to review.
Google BigQuery
BigQuery is Google Cloud’s data warehouse for analytical queries.
-
Define the question
Choose the project, table, region, and period.
-
Run the analysis
Inspect the columns and query the relevant records.
-
Review the result
Summarize the completed query with its scope.
What to Ask Leina to Do with Google BigQuery
Copy a prompt, add your details, and send it to Leina in your connected team chat.
Answer a Data Question with SQL
In project [project], region [region], use table [table] to answer [question] for [date range]. Run a read-only query and summarize the completed results.
google_bigquery
Save Query Results in Google Sheets
In BigQuery project [project] and region [region], use [table] to summarize [question] for [date range]. Save the completed summary in [spreadsheet link], worksheet [name], replacing only [target cell range].
google_bigquery + Google Sheets
Your next Google BigQuery task can start with a message.
Connect. Ask. Get the Result.
Tell Leina what you need in your team chat. Include the source, destination, and expected result.
-
Connect Your Account
Connect the tools involved and give Leina access to the content it needs.
-
Try a Task
Share the source material, say where the result belongs, and describe what you need.
-
Check the Result
Review the result and any changes in the connected app.
Put Leina to Work in Google BigQuery
Hand off these everyday tasks to your AI employee.
List Projects
List Google Cloud projects accessible to BigQuery.
List Datasets
List BigQuery datasets in a Google Cloud project.
Get Dataset
Retrieve BigQuery dataset metadata.
List Tables
List BigQuery tables in a dataset.
Get Table
Retrieve BigQuery table metadata, including schema when available.
List Table Data
List rows from a BigQuery table.
Query
Run a BigQuery SQL query and return the first page of results.
Get Query Results
Poll a BigQuery query job and return a page of results.
Get Job
Retrieve BigQuery job metadata.
List Jobs
List BigQuery jobs in a Google Cloud project.
Cancel Job
Cancel a BigQuery job.
Start Query Job
Start an asynchronous BigQuery query job.
Start Load Job From Gcs
Start an asynchronous BigQuery load job from Cloud Storage objects.
Start Extract Job To Gcs
Start an asynchronous BigQuery extract job to Cloud Storage objects.
Create Dataset
Create a BigQuery dataset.
Patch Dataset
Patch BigQuery dataset metadata.
Update Dataset
Replace BigQuery dataset metadata with the supplied dataset resource fields.
Delete Dataset
Delete a BigQuery dataset, optionally deleting contained tables when explicitly requested.
Create Table
Create a BigQuery table.
Patch Table
Patch BigQuery table metadata.
Update Table
Replace BigQuery table metadata with the supplied table resource fields.
Delete Table
Delete a BigQuery table from a dataset.
Insert All
Insert a small batch of rows into a BigQuery table.
List Routines
List BigQuery routines in a dataset.
Get Routine
Retrieve BigQuery routine metadata.
Create Routine
Create a BigQuery routine such as a user-defined function or stored procedure.
Update Routine
Replace BigQuery routine metadata and definition fields.
Delete Routine
Delete a BigQuery routine from a dataset.
List Models
List BigQuery ML models in a dataset.
Get Model
Retrieve BigQuery ML model metadata.
Patch Model
Patch BigQuery ML model metadata such as friendly name, description, or labels.
Delete Model
Delete a BigQuery ML model from a dataset.
Let Google BigQuery Work with Your Other Tools
Tell Leina which sources to use and where the result belongs.
More Tools for Your AI Employee
Connect the Apps your work depends on, and give Leina the context to carry the task forward.
Keep the Conversation Moving. Let Leina Pick Up the Work.
Hand off a task while your team is discussing it. Leina uses your connected tools and brings the result back to the conversation.
See How Leina Works in SlackLeinaExample Result I queried completed orders for the requested month and grouped the results by channel.
Completed orders
Google BigQuery + Leina: Your Questions, Answered
Can I check a query before running it?
Yes. Request a dry run to validate the SQL before deciding whether to execute it.
What access does the assistant need?
The connected account must be able to read the selected tables and run query jobs in the project.
Make Leina Your Google BigQuery AI Employee
Start with one task you already need to do. Tell Leina what a finished result should look like.
Connect Google BigQuery, choose what to share, and hand off your first task.