bigquery

Query BigQuery INFORMATION_SCHEMA tables and columns for metadata without exposing data.

1|Updated Nov 15, 2022

Search Tags:#metadata #sql #bigquery #dataset #bq #information_schema #data-sensitivity

Ready to run

npx skills add https://github.com/BfdCampos/dotfiles --skill bigquery-bfdcampos

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: bigquery
Source: https://github.com/BfdCampos/dotfiles/tree/main/claude/skills/bigquery
Command: npx skills add https://github.com/BfdCampos/dotfiles --skill bigquery-bfdcampos

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

BigQuery projects often require understanding data structures without exposing sensitive contents. This skill provides safe metadata queries to preview schemas and table metadata.

Core Features & Use Cases

INFORMATION_SCHEMA queries to inspect column names, data types, and table names without returning actual data.
Count and list datasets/tables to validate schemas and assist data-modeling workflows (dbt, data pipelines).
Use cases include auditing dataset schemas, validating source structures for migrations, and quick metadata discovery across monzo-analytics projects.

Quick Start

Run a basic information_schema query to inspect the dimensions and columns of a target dataset without returning actual data.

Frequently Asked Questions about bigquery

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I query BigQuery table schemas without exposing actual data?▼

Use standard SQL to query INFORMATION_SCHEMA.TABLES and INFORMATION_SCHEMA.COLUMNS to retrieve BigQuery table schemas without returning actual data. This safely exposes column names, data types, and table structures while keeping sensitive contents hidden.

What is the best way to audit BigQuery dataset structures safely?▼

Audit BigQuery dataset structures safely by querying INFORMATION_SCHEMA views to list and count datasets and tables. This validates schemas for data modeling workflows without exposing sensitive data contents.

Can I use BigQuery metadata queries to validate source schemas for dbt pipelines?▼

Yes, validate source schemas for dbt pipelines by running BigQuery metadata queries against INFORMATION_SCHEMA.COLUMNS. This inspects column names and data types to validate source structures for migrations and data pipelines.

Do I need to specify a project ID when running BigQuery metadata queries?▼

Yes, specify a project ID when running BigQuery metadata queries by always including the --project_id flag. You must also prescribe using standard SQL with the --use_legacy_sql=false flag.

Why should I use INFORMATION_SCHEMA instead of SELECT * for BigQuery data discovery?▼

Use INFORMATION_SCHEMA instead of SELECT * for BigQuery data discovery because it reveals schemas and table structures without exposing actual data. This prevents returning sensitive data during dataset auditing and metadata discovery.