Overview
Google Cloud BigQuery data warehouse for analytics and machine learning. Through Langdock’s integration, you can access and manage Big Query directly from your conversations.Authentication: OAuth
Category: Data & Analytics
Availability: All workspace plans
Category: Data & Analytics
Availability: All workspace plans
Available Actions
List Datasets
bigquery.listDatasets
Lists all datasets in a BigQuery project
Requires Confirmation: No
Parameters:
projectId
(TEXT, Required): The Google Cloud project ID containing the datasets
List Tables
bigquery.listTables
Lists all tables in a BigQuery dataset
Requires Confirmation: No
Parameters:
projectId
(TEXT, Required): The Google Cloud project IDdatasetId
(TEXT, Required): The dataset ID containing the tables
Get Table Schema
bigquery.getTableSchema
Gets the schema information for a specific BigQuery table
Requires Confirmation: No
Parameters:
projectId
(TEXT, Required): The Google Cloud project IDdatasetId
(TEXT, Required): The dataset ID containing the tabletableId
(TEXT, Required): The table ID to get schema information for
Execute Query
bigquery.executeQuery
Executes a SQL query in BigQuery and returns the results
Requires Confirmation: No
Parameters:
projectId
(TEXT, Required): The Google Cloud project ID to execute the query inquery
(MULTI_LINE_TEXT, Required): The SQL query to execute in BigQueryuseLegacySql
(BOOLEAN, Optional): Whether to use legacy SQL syntax (default: false for Standard SQL)
jobReference
: Job reference informationtotalRows
: Total number of rows in the resultrows
: Array of result rows containing field valuesschema
: Schema of the result fieldsjobComplete
: Whether the job completed successfully
Get Table Data
bigquery.getTableData
Retrieves actual data rows from a BigQuery table
Requires Confirmation: No
Parameters:
projectId
(TEXT, Required): The Google Cloud project IDdatasetId
(TEXT, Required): The dataset ID containing the tabletableId
(TEXT, Required): The table ID to retrieve data frommaxResults
(NUMBER, Optional): Maximum number of rows to return (optional)
Create Dataset
bigquery.createDataset
Creates a new dataset in BigQuery
Requires Confirmation: No
Parameters:
projectId
(TEXT, Required): The Google Cloud project IDdatasetId
(TEXT, Required): The ID for the new datasetdescription
(TEXT, Optional): Optional description for the datasetlocation
(TEXT, Optional): Geographic location for the dataset (e.g., US, EU)
Create Table
bigquery.createTable
Creates a new table in a BigQuery dataset
Requires Confirmation: No
Parameters:
projectId
(TEXT, Required): The Google Cloud project IDdatasetId
(TEXT, Required): The dataset ID to create the table intableId
(TEXT, Required): The ID for the new tabledescription
(TEXT, Optional): Optional description for the tableschema
(MULTI_LINE_TEXT, Optional): Table schema as JSON array of field objects (optional)
Insert Table Data
bigquery.insertTableData
Inserts data rows into a BigQuery table
Requires Confirmation: No
Parameters:
projectId
(TEXT, Required): The Google Cloud project IDdatasetId
(TEXT, Required): The dataset ID containing the tabletableId
(TEXT, Required): The table ID to insert data intorows
(MULTI_LINE_TEXT, Required): JSON array of row objects to insertignoreUnknownValues
(BOOLEAN, Optional): Whether to ignore unknown values in the dataskipInvalidRows
(BOOLEAN, Optional): Whether to skip rows that contain invalid data
Get Dataset Info
bigquery.getDatasetInfo
Gets detailed information about a BigQuery dataset
Requires Confirmation: No
Parameters:
projectId
(TEXT, Required): The Google Cloud project IDdatasetId
(TEXT, Required): The dataset ID to get information for
Common Use Cases
Data Management
Manage and organize your Big Query data
Automation
Automate workflows with Big Query
Reporting
Generate insights and reports
Integration
Connect Big Query with other tools
Best Practices
Getting Started:
- Enable the Big Query integration in your workspace settings
- Authenticate using OAuth
- Test the connection with a simple read operation
- Explore available actions for your use case
Important Considerations:
- Ensure proper authentication credentials
- Respect rate limits and API quotas
- Review data privacy settings
- Test operations in a safe environment first
Troubleshooting
Issue | Solution |
---|---|
Authentication failed | Verify your OAuth credentials |
Rate limit exceeded | Reduce request frequency |
Data not found | Check permissions and data availability |
Connection timeout | Verify network connectivity |