ceven

Google BigQuery

Runs complex SQL queries against your data warehouse, extracts specific result sets for reporting, and pushes processed data into downstream tools.

Try Google BigQuery in Ceven

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Why use Ceven?

  1. AI native Google BigQuery integration

    • Describe the outcome and Ceven picks the right Google BigQuery calls, fills the parameters, and checks the result.
    • Structured, agent friendly tool schemas so each call runs reliably instead of by guesswork.
    • Rich coverage for reading, writing, and querying your Google BigQuery data, across all 63 of its actions.

  2. Managed auth

    • Built in OAuth with automatic token refresh and rotation.
    • One place to manage, scope, and revoke Google BigQuery access.
    • Per user and per environment credentials instead of shared keys.

  3. Agent optimized design

    • Actions are tuned from real success and error rates so reliability climbs over time.
    • Full execution logs so you always know what ran in Google BigQuery, when, and on whose behalf.
    • The agent pauses and asks when Google BigQuery is unclear instead of plowing ahead.

  4. Enterprise grade security

    • Fine grained access so you control which agents and people can reach Google BigQuery.
    • Least privilege by default, read scopes first and only the writes a workflow needs.
    • A full audit trail of every Google BigQuery action to support review and sign off.

Supported tools

Every action Ceven's agents can run on Google BigQuery, and when to use it.

Run query
Use this to execute a SQL query and pull results. Keep the query on a single line for best results.
Get table metadata
Pull the schema, column names, and data types for a specific table to inform further queries.
List datasets
Pull a list of all available datasets in a project to find the correct data source.
List tables
Pull all tables within a specific dataset to identify where a specific metric is stored.
Insert rows
Push new data records into a BigQuery table for logging or archival purposes.
Update table
Modify the metadata or properties of an existing table.
Delete table
Remove a table from a dataset when it is no longer needed for analysis.
Create dataset
Initialize a new dataset container to organize new tables and views.
Get job status
Check if a long running query has finished or failed to ensure data is ready.
Cancel job
Stop a running query to prevent excessive slot usage or cost overruns.
Export data
Move query results from BigQuery to a Google Cloud Storage bucket.
Search tables
Find tables across a project that contain specific column names or patterns.
Query
Query tool will run a sql query in bigquery. note: make sure the query being input in a single line format. for example, select * from sample dataset.sample table where column name = 'value'

13 actions · scroll to see them all

Frequently asked questions

Ceven uses OAuth 2.0 to connect to your Google Cloud project. When you initiate the connection, you are redirected to the Google consent screen where you select the account and grant specific permissions for BigQuery access. We store the encrypted refresh token to maintain the connection and generate a short lived access token for every single request. This ensures that your credentials are never stored in plain text and you can revoke access instantly through your Google Account security settings. The agent only accesses the data it needs to fulfill the specific workflow you have designed.
Yes. The agent can generate the SQL syntax based on your natural language request. However, it needs to know the schema of your tables to be accurate. We recommend using the Get table metadata action first so the agent knows exactly which columns exist and what the data types are. Once the agent has the schema, it constructs a single line SQL query and executes it. If the query returns an error, the agent reads the error message from Google and attempts to rewrite the query to fix the syntax automatically.
Ceven is subject to the standard BigQuery API quotas and the specific limits of your Google Cloud project. A critical quirk to note is the maximum response size for the API; if a query returns millions of rows, the API will paginate the results. Ceven handles this pagination by walking the page tokens until the requested limit is met or the data is exhausted. However, to prevent timeouts and high costs, we suggest using WHERE clauses to filter data as much as possible before pulling it into the workflow.
Since BigQuery charges based on the amount of data scanned for on demand pricing, any query run by Ceven will count toward your usage. To minimize costs, the agent is programmed to avoid SELECT * queries whenever possible and instead request only the specific columns needed for the task. You can also set up maximum bytes billed limits in your Google Cloud Console to prevent any single query from exceeding a certain cost threshold, which provides a hard safety rail for all agent activities.
Ceven operates using the permissions granted to the OAuth token during the connection process. If the account that authorized the connection has the BigQuery Admin role, the agent can perform management tasks like creating or deleting tables. If the account only has BigQuery Data Viewer permissions, the agent will be limited to read only actions. We recommend using a dedicated service account or a user with the least privilege necessary to perform the specific business workflow you are automating to maintain a strong security posture.
BigQuery queries are asynchronous by nature. When Ceven submits a query, Google returns a job ID. The agent then enters a polling loop, using the Get job status action to check if the query is still running, has succeeded, or has failed. If the query takes a long time, the agent will wait and retry the status check at increasing intervals. If the job exceeds the timeout limit set in your workflow configuration, the agent will mark the step as failed and notify you via the configured alert channel.
Ceven treats your BigQuery data as transient. When the agent pulls a result set to perform a task, such as sending an email or updating a CRM, that data lives in the volatile memory of the workflow execution. Once the workflow completes and the final action is confirmed, the temporary data is purged. We do not use your warehouse data to train our models or store it in a permanent database. Your data remains in your Google Cloud environment and is only accessed on a per request basis.
Yes, the agent can execute DDL statements to create views if the connected account has the appropriate permissions. This is particularly useful for simplifying complex joins into a single virtual table that the agent can query more efficiently in subsequent steps. You can tell the agent to create a view for a specific reporting period or a specific customer segment. This reduces the complexity of the SQL that needs to be sent in every single call and can help in reducing the overall data scanned per query.

Alternatives to Google BigQuery

Other tools that solve a similar problem. Ceven supports these too, so you can switch or run more than one at once.

Snowflake logoSnowflakeAmazon Redshift logoAmazon RedshiftDatabricks logoDatabricks

Try Ceven on your stack

Plug Ceven on top of the tools you already run. Connect Google BigQuery and the rest of your stack, describe the outcome, and its agents handle the work end to end, days of it in minutes.

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