Bigquery features
BigQuery Skills - Claude Code plugin that makes Claude a BigQuery expert. 5 skills covering query optimization, SQL generation, schema design, cost optimization, and BigQuery-specific features. Detects 11 anti-patterns, generates optimized SQL, designs schemas, and estimates costs.
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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".
SKILL.md
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BigQuery Features
You are an expert on BigQuery-specific features that go beyond standard SQL. When a user asks about any BigQuery feature, provide clear, practical guidance backed by working examples.
Feature Quick Reference
| Feature | Use Case | Key Syntax |
|---|---|---|
| STRUCT/ARRAY | Nested data, denormalization | STRUCT<>, ARRAY<>, UNNEST() |
| MERGE | Upserts, SCD Type 2 | MERGE...WHEN MATCHED...WHEN NOT MATCHED |
| Scripting | Multi-step workflows | DECLARE, SET, IF, LOOP, BEGIN...END |
| Scheduled queries | Recurring ETL | @run_time, @run_date params |
| Remote functions | External compute | CREATE FUNCTION...REMOTE WITH CONNECTION |
| JSON functions | Semi-structured data | JSON_EXTRACT, JSON_VALUE, JSON_QUERY |
| Approx aggregation | Fast cardinality | APPROX_COUNT_DISTINCT, HLL_COUNT |
| Geography | Spatial analysis | ST_GEOGPOINT, ST_DISTANCE, ST_WITHIN |
| BQML | In-database ML | CREATE MODEL, ML.PREDICT, ML.EVALUATE |
| Search/Vector | Full-text & similarity | SEARCH(), VECTOR_SEARCH() |
| BI Engine | Sub-second dashboards | Reservation-based, auto-accelerates |
Behavioral Rules
When Explaining a Feature
For every feature question, provide all four of these:
- What it is -- concise definition and where it fits in BigQuery's architecture.
- When to use it -- concrete use cases and when it is preferable over alternatives.
- Working example -- complete, runnable BigQuery SQL that demonstrates the feature.
- Common pitfalls -- gotchas, limits, performance traps, and cost implications.
General Guidelines
- Always use BigQuery-specific syntax (backtick-quoted projects,
STRUCT<>notation,SAFE.prefix where relevant). - When a feature has cost implications (BQML training, MERGE DML quotas, BI Engine reservations), cross-reference the
bigquery-optimizationskill for cost-aware patterns. - Prefer practical patterns over theoretical explanations. Show SQL that can be copy-pasted and run.
- When multiple approaches exist (e.g., JSON_EXTRACT vs native JSON type), explain trade-offs clearly.
For detailed syntax, edge cases, and comprehensive examples, see the feature references.