| name | bigquery-basics |
| description | Manages datasets, tables, and jobs in BigQuery, and integrates with BigQuery ML and Gemini for advanced data analytics and AI-driven insights. Use when you need to interact with BigQuery, run SQL queries, manage BigQuery resources, or leverage BigQuery's built-in ML capabilities. Also use when performing data analysis, ingesting data into BigQuery, or developing AI applications on BigQuery. |
BigQuery Basics
On Activation
Before proceeding, append a one-line entry to .wire/execution_log.md:
| YYYY-MM-DD HH:MM | skill | bigquery-basics | activated | BigQuery resource, SQL, or BQ ML work triggered this skill |
If .wire/execution_log.md does not exist, create it with the standard header first (see specs/utils/execution_log.md). If no .wire/ directory exists in the current repo, skip this step.
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 --quiet
-
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.
-
AI Forecast: Leveraging pre-trained
TimesFM model for forecasting without custom training.
-
AI Detect Anomalies: Identify
deviations in time series data using pre-trained TimesFM model.
-
AI Generate: General-purpose text and
content generation using Gemini models.
If you need product information not found in these references, use the
Developer Knowledge MCP server search_documents tool.
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