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A Python-based tool for BigQuery, Google Cloud's fully managed and completely serverless enterprise data warehouse. More information:

  • Run query against a BigQuery table using standard SQL, add --dry_run flag to estimate the number of bytes read by the query:

bq query --nouse_legacy_sql 'SELECT COUNT(*) FROM {{DATASET_NAME}}.{{TABLE_NAME}}'

  • Run a parameterized query:

bq query --use_legacy_sql=false --parameter='ts_value:TIMESTAMP:2016-12-07 08:00:00' 'SELECT TIMESTAMP_ADD(@ts_value, INTERVAL 1 HOUR)'

  • Create a new dataset or table in the US location:

bq mk --location=US {{dataset_name}}.{{table_name}}

  • List all datasets in a project:

bq ls --filter labels.{{key}}:{{value}} --max_results {{integer}} --format=prettyjson --project_id {{project_id}}

  • Batch load data from a specific file in formats such as CSV, JSON, Parquet, and Avro to a table:

bq load --location {{location}} --source_format {{CSV|JSON|PARQUET|AVRO}} {{dataset}}.{{table}} {{path_to_source}}

  • Copy one table to another:

bq cp {{dataset}}.{{OLD_TABLE}} {{dataset}}.{{new_table}}

  • Display help:

bq help