bigquery-expert

Design a BigQuery table schema with optimal partitioning, clustering, and data types. Describe your data or paste a sample.

Estimate the cost of a BigQuery query or table. Pass a query, file path, or table description.

Explain a BigQuery feature, function, or concept with working examples. Pass the feature name or question.

Generate optimized BigQuery SQL from a natural language description. Pass a description of the data you need.

Optimize BigQuery SQL by fixing all detected anti-patterns. Pass a file path or paste SQL directly.

Review BigQuery SQL for performance anti-patterns. Pass a file path or paste SQL directly.

Use when asked to analyze BigQuery SQL files across a project for cost optimization opportunities, estimate total query costs, or audit a codebase for expensive query patterns. <example>Analyze all my SQL files for cost optimization opportunities</example> <example>Which queries in this project are the most expensive?</example> <example>Audit my BigQuery queries for cost reduction</example>

Use when asked to review all SQL files in a project or directory for BigQuery anti-patterns, scan a codebase for SQL performance issues, or audit BigQuery queries across multiple files. <example>Scan all SQL files in this project for BigQuery anti-patterns</example> <example>Audit my BigQuery queries for performance issues</example> <example>Review all the SQL in this repo and tell me what to optimize</example>

Use when asked to analyze table schemas across a project, recommend partitioning and clustering strategies for existing tables, audit schema design, or optimize table structure across a dataset or project. <example>Analyze my BigQuery schemas and recommend partitioning strategies</example> <example>Audit the table designs in this project and suggest improvements</example> <example>Review my DDL files and optimize the schema design</example>

Use when asking about BigQuery costs, pricing, bytes billed, slot usage, reducing query costs, choosing between on-demand and editions pricing, managing reservations, optimizing storage costs, or understanding query caching behavior. Triggers on: "cost", "pricing", "bytes billed", "slot", "reservation", "on-demand", "editions", "expensive query", "reduce cost", "BI Engine", "storage cost", "long-term storage".

Use when asking about BigQuery-specific features, syntax, or capabilities including: STRUCT/ARRAY/UNNEST patterns, MERGE statements, BigQuery scripting (DECLARE, IF, LOOP, BEGIN/END), scheduled queries, remote functions, JSON functions, approximate aggregation (APPROX_COUNT_DISTINCT, HLL_COUNT), geography/GIS functions, BigQuery ML (CREATE MODEL), search indexes, vector search, or BI Engine. Triggers on: "UNNEST", "STRUCT", "ARRAY", "MERGE", "DECLARE", "scripting", "scheduled query", "remote function", "JSON_EXTRACT", "APPROX_COUNT", "HLL", "ST_", "CREATE MODEL", "BQML", "search index", "vector search", "BI Engine".

Use when writing, reviewing, or optimizing BigQuery SQL, asking about BigQuery best practices, working with .sql files targeting BigQuery, or troubleshooting slow/expensive BigQuery queries. Symptoms: high slot consumption, full table scans, expensive joins, slow queries, high bytes billed.

Use when generating BigQuery SQL from natural language descriptions, converting queries from other SQL dialects to BigQuery, writing new BigQuery queries from scratch, or when the user describes what data they need and expects SQL output. Triggers on: "write me a query", "generate SQL", "how do I query", "convert this to BigQuery", "I need to get data from", "create a query".

Use when designing BigQuery table schemas, choosing partitioning or clustering strategies, deciding between nested/repeated fields vs flat schemas, selecting table types (native, external, views, materialized views), choosing data types, or planning denormalization. Triggers on: "partition", "cluster", "STRUCT", "ARRAY", "nested fields", "table design", "schema", "materialized view", "external table", "denormalize", "data type", "TIMESTAMP vs DATETIME".