richardhe-fundamenta/practical-gcp-examples/bigquery-basics
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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.
Install
npx vskill@latest install richardhe-fundamenta/practical-gcp-examples/bigquery-basicsSKILL.md
BigQuery Basics
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:
``bash gcloud services enable bigquery.googleapis.com --quiet ``
- Create a Dataset:
``bash bq mk --dataset --location=US my_dataset ``
- Create a Table:
Create a file named schema.json with your table schema:
``json [ { "name": "name", "type": "STRING", "mode": "REQUIRED" }, { "name": "post_abbr", "type": "STRING", "mode": "NULLABLE" } ] ``
Then create the table with the bq tool:
``bash bq mk --table my_dataset.mytable schema.json ``
- Run a Query:
``bash 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
bqcommand-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.
Related Skills
SKILL.md file for BigQuery AI and ML capabilities.
Reference files published for the BigQuery AI and ML skill.
Security
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Source
- SKILL.md in the repositoryCheckingadk-agy-agent/skills/bigquery-basics/SKILL.md
Popularity
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- 41
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- 17
- 7-day trend score
- 19
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