BigQuery is a serverless, AI-ready data platform that enables high-speed
analysis of large datasets using SQL and Python. Its disaggregated architecture
separates compute and storage, allowing them to scale independently while
providing built-in machine learning, geospatial analysis, and business
intelligence capabilities.
Setup and Basic Usage
Enable the BigQuery API:
gcloud services
enable
bigquery.googleapis.com
Create a Dataset:
bq mk
--dataset
--location
=
US my_dataset
Create a Table:
Create a file named
schema.json
with your table schema:
[
{
"name"
:
"name"
,
"type"
:
"STRING"
,
"mode"
:
"REQUIRED"
}
,
{
"name"
:
"post_abbr"
,
"type"
:
"STRING"
,
"mode"
:
"NULLABLE"
}
]
Then create the table with the
bq
tool:
bq mk
--table
my_dataset.mytable schema.json
Run a Query:
bq query
--use_legacy_sql
=
false
\
'SELECT name FROM bigquery-public-data.usa_names.usa_1910_2013 \
WHERE state = "TX" LIMIT 10'
Reference Directory
Core Concepts
Storage types, analytics
workflows, and BigQuery Studio features.
CLI Usage
Essential
bq
command-line tool
operations for managing data and jobs.
Client Libraries
Using Google Cloud
client libraries for Python, Java, Node.js, and Go.
MCP Usage
Using the BigQuery remote MCP server and
Gemini CLI extension.
Infrastructure as Code
Terraform examples for
datasets, tables, and reservations.
IAM & Security
Roles, permissions, and data
governance best practices.
If you need product information not found in these references, use the
Developer Knowledge MCP server
search_documents
tool.