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bigquery-basics

Manages BigQuery datasets, tables, and jobs using SQL, bq CLI, and client libraries.

Updated May 11, 2026
Search Tags:#analytics#gcp#sql#terraform#data warehouse#bigquery#bq cli
One-click install
npx skills add https://github.com/alon3153/upe-social-publisher --skill bigquery-basics-alon3153
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: bigquery-basics
Source: https://github.com/alon3153/upe-social-publisher/tree/main/.agents/skills/bigquery-basics
Command: npx skills add https://github.com/alon3153/upe-social-publisher --skill bigquery-basics-alon3153

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve? Working with BigQuery requires knowing the right commands, APIs, and configuration patterns across many tools. This Skill consolidates the essential operations for managing datasets, tables, and jobs so you can run queries and administer BigQuery resources without searching through scattered documentation. ## Core Features & Use Cases - Resource Management: Create datasets and tables, load data from Cloud Storage, and manage jobs using the bq command-line tool or Terraform. - Query Execution: Run standard SQL queries, perform dry runs to estimate bytes processed, and track incremental table changes with APPENDS and CHANGES functions. - Real-Time & Integrations: Set up continuous queries for streaming analysis, connect via the BigQuery MCP server, and apply IAM security controls like row-level and column-level access. - Use Case: A data engineer needs to create a new dataset, load CSV files from Cloud Storage, and run a scheduled query. This Skill provides the exact bq commands, Terraform configuration, and IAM roles required for each step. ## Quick Start Ask the assistant to create a BigQuery dataset and run a SQL query against a public dataset using the bq command-line tool.

Frequently Asked Questions about bigquery-basics

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

FAQPage Schema
How do I create a dataset and table in BigQuery?▼

Use the bq CLI: run bq mk --dataset --location=US my_dataset to create a dataset, then bq mk --table my_dataset.mytable schema.json with a JSON schema file to create a table. Terraform resources google_bigquery_dataset and google_bigquery_table offer an IaC alternative.

How to run a SQL query in BigQuery from the command line?▼

Run bq query --use_legacy_sql=false followed by your standard SQL statement. Add the --dry_run flag to estimate bytes processed before execution, and use --location and --project_id flags to target specific resources.

bq CLI vs BigQuery client libraries, which should I use?▼

The bq CLI suits scripting and ad hoc operations like loading data or managing jobs. Client libraries for Python, Java, Node.js, and Go fit application code, while bigframes provides a pandas-like API that executes directly in BigQuery for Python users.

Does BigQuery support tracking incremental table changes?▼

Yes, the APPENDS function returns rows appended in a time range, and CHANGES returns inserts, updates, and deletes. CHANGES requires setting enable_change_history = TRUE on the table first; APPENDS works without it.

What are the limitations of BigQuery continuous queries?▼

Continuous queries run by user accounts stop after two days; service accounts allow up to 150 days. They require an Enterprise or Enterprise Plus reservation with a CONTINUOUS job type, and support only limited stateful operations like specific joins and window aggregations.

Can I run INSERT or UPDATE statements through the BigQuery MCP server?▼

No, the execute_sql tool in the BigQuery MCP server is restricted to SELECT statements only. INSERT, UPDATE, DELETE, and stored procedures return an error, though queries invoking remote functions or Python UDFs may still have side effects.

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