القائمة
Query and manage Google BigQuery datasets -- run SQL, list tables, inspect schemas, load data, and manage partitioning.
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استنادا إلى تصنيف SOC المهني
Manage Azure cloud resources -- resource groups, storage, databases, functions, and data services using the az CLI.
Work with Databricks -- run SQL via curl, manage clusters, warehouses, Unity Catalog, jobs, and pipelines.
Query and manage PostgreSQL databases -- run SQL, inspect schemas, manage tables, and perform database administration.
Query and manage Snowflake data warehouses -- run SQL, manage warehouses, inspect schemas, and control access.
Generate Airflow DAGs from pipeline specifications using project templates.
Build and manage Dagster data pipelines -- create assets, jobs, schedules, sensors, and resources.
| name | bigquery |
| description | Query and manage Google BigQuery datasets -- run SQL, list tables, inspect schemas, load data, and manage partitioning. |
| metadata | {"openclaw":{"emoji":"🔍","requires":{"bins":["bq"]},"tags":["database","bigquery","sql","google","cloud","data"],"secrets":[{"env_var":"GOOGLE_APPLICATION_CREDENTIALS","label":"GCP Service Account Key Path","placeholder":"/path/to/service-account.json","optional":true},{"env_var":"GOOGLE_CLOUD_PROJECT","label":"GCP Project ID","placeholder":"my-project-id"}]}} |
You can query and manage Google BigQuery datasets using the bq CLI.
Use this when the user asks about BigQuery tables, wants to run SQL, or needs to manage datasets.
Ensure the user has authenticated with gcloud auth application-default login or has a service account key configured via GOOGLE_APPLICATION_CREDENTIALS.
bq ls --project_id=<project_id>
bq ls <project_id>:<dataset>
bq show --schema --format=prettyjson <project_id>:<dataset>.<table>
bq query --use_legacy_sql=false --format=prettyjson 'SELECT * FROM `<project>.<dataset>.<table>` LIMIT 10'
bq query --use_legacy_sql=false 'SELECT COUNT(*) AS row_count FROM `<project>.<dataset>.<table>`'
bq load --autodetect --source_format=CSV <project>:<dataset>.<table> <path/to/file.csv>
bq load --autodetect --source_format=NEWLINE_DELIMITED_JSON <project>:<dataset>.<table> <path/to/file.json>
bq query --use_legacy_sql=false --destination_table=<project>:<dataset>.<table> --time_partitioning_field=<date_column> 'SELECT * FROM `<source_table>`'
bq show --format=prettyjson <project>:<dataset>.<table>
--use_legacy_sql=false)LIMIT for exploratory queries to control costsbq query --dry_run to estimate query cost before running