Skill v1.0.1

version: "1.0.1" name: developing-with-bigquery description: | Provides BigQuery-specific logic, knowledge, and specialized standards. Use this skill whenever you are doing anything with BigQuery, including:

1. BigQuery query optimization (SQL)
2. BigFrames Python code
3. BigQuery ML/AI functions (SQL & Python)
4. Graph Analytics (GQL & Property Graphs)

license: Apache-2.0 metadata: version: v2 publisher: google

This skill provides comprehensive guidance for BigQuery services, optimizations, and data handling. It acts as a routing table for specialized BigQuery topics.

[!IMPORTANT]

For general standards on running BigQuery in notebooks (SQL cells, export

keyword), see @skill:notebook-guidance.

[!IMPORTANT]

You MUST check the data size before deciding on which libraries to use. Use

the data size to justify your decision.

Refer to the following resources for expert guidance on specific BigQuery features:

1. Query Optimization

Performance and efficiency guidelines for BigQuery SQL. Includes rules for column pruning, pushdown, and materialization strategies.

• Guide: OPTIMIZATION.md

2. BigFrames (BigQuery DataFrames)

Guidelines for generating valid BigFrames code for data manipulation, model development, and visualization.

• Guide: BIGFRAMES.md

Bigframes should be the default library/tool as it is more efficient than using the BigQuery Python client library.

3. BigQuery ML & AI Functions (BQML SQL)

Usage rules and syntax standards for all BigQuery AI/ML functions via SQL (Forecasting, Generative AI, Classification, etc.).

• Best Practices: ai_function_best_practices.md
• Functions Reference:

• AI.CLASSIFY: ai_classify.md - Classify text.
• AI.DETECT_ANOMALIES: ai_detect_anomalies.md - Detect anomalies.
• AI.EVALUATE: ai_evaluate.md - Evaluate models.
• AI.FORECAST: ai_forecast.md - Time-series forecasting.
• AI.GENERATE: ai_generate.md - Generate text using LLMs.
• AI.GENERATE_EMBEDDING: ai_generate_embedding.md - Generate embeddings.
• AI.GENERATE_TABLE: ai_generate_table.md - Table-valued AI generation.
• AI.IF: ai_if.md - Evaluate semantic conditions.
• AI.KEY_DRIVERS: ai_key_drivers.md - Identify key drivers.
• AI.SCORE: ai_score.md - Score data.
• AI.SEARCH: ai_search.md - Semantic search.
• AI.SIMILARITY: ai_similarity.md - Semantic similarity.
• Remote Models: remote_models.md - Working with remote models (Vertex AI).
• CONTRIBUTION_ANALYSIS: ml_contribution_analysis.md - Step-by-step contribution analysis.
• VECTOR_SEARCH: vector_search.md - Vector search best practices.

4. Graph Analytics (Property Graphs & GQL)

Guidelines and best practices for querying property graphs in BigQuery.

• Property Graph Guidelines: graph_queries.md - Standard GQL syntax and query patterns.
• Semantic Graph Guidelines: semantic_queries.md - Semantic graph operations and expand functions.