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name: bigquery description: Instructions for querying Google BigQuery using the bq command-line tool. Useful for running SQL queries, exploring datasets, and exporting results.

BigQuery Skill

This skill enables you to query Google BigQuery using the bq command-line tool.

Authentication is already configured. The default project is project_id.

Use the project_id_exploration dataset as scratch space for temporary tables and experimentation.

The public dataset contains tables synced from the production Postgres database.

1. Explore Available Datasets

List datasets in the current project (defaults to project_id):

Copybq ls

List datasets in a specific project:

Copybq ls --project_id=PROJECT_ID

List tables in a dataset:

Copybq ls PROJECT_ID:DATASET_NAME

Get table schema:

Copybq show --schema --format=prettyjson PROJECT_ID:DATASET_NAME.TABLE_NAME

2. Run Queries

Run a simple query:

Copybq query --use_legacy_sql=false 'SELECT * FROM `project.dataset.table` LIMIT 10'

Run a query with formatted output:

Copybq query --use_legacy_sql=false --format=prettyjson 'YOUR_QUERY'

Run a query and save to a destination table:

Copybq query --use_legacy_sql=false --destination_table=PROJECT:DATASET.NEW_TABLE 'YOUR_QUERY'

Run a dry run to estimate costs:

Copybq query --use_legacy_sql=false --dry_run 'YOUR_QUERY'

3. Export Results

Export to CSV:

Copybq query --use_legacy_sql=false --format=csv 'YOUR_QUERY' > results.csv

4. Best Practices

• Always use --use_legacy_sql=false for standard SQL syntax
• Use LIMIT clauses when exploring data to reduce costs
• Use --dry_run to estimate query costs before running expensive queries
• Use backticks around table references: `project.dataset.table`

5. Common Patterns

Preview table data:

Copybq head -n 10 PROJECT:DATASET.TABLE

Get table info (row count, size):

Copybq show --format=prettyjson PROJECT:DATASET.TABLE

Query with parameters:

Copybq query --use_legacy_sql=false --parameter='name:STRING:value' 'SELECT * FROM `table` WHERE col = @name'