| name | developing-with-bigquery |
| description | A repository of BigQuery-specific logic, knowledge, and specialized standards.
Use this skill whenever you are doing anything with BigQuery, including:
1. BigQuery query optimization
2. BigFrames Python code
3. BigQuery ML/AI functions.
|
| license | Apache-2.0 |
| metadata | {"version":"v1","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.). - Guide:
BQML.md - Functions Reference: -
AI.FORECAST -
AI.EVALUATE -
AI.GENERATE_TABLE -
AI.GENERATE_EMBEDDING -
Remote Models
CONTRIBUTION_ANALYSIS
VECTOR_SEARCH
4. Notebook SQL cells
Refer to @skill:notebook-guidance for standards on running BigQuery in
notebooks.
