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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.
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基于 SOC 职业分类
Discovers and inspects BigQuery Data Transfer Service (DTS) configurations. Use this to identify existing ingestion pipelines and extract datasource or transfer config metadata for data pipelines. Use when a user asks for ingestion scenarios while building or managing data pipelines or when a user asks to "ingest" or "add" data that may already be managed by a DTS transfer.
Build modern data apps, dashboards, and interactive reports using either React + Vite or Streamlit. Includes optional Gemini Data Analytics chat integration for an AI powered "chat with your data" experience. Relevant when any of the following conditions are true: 1. User explicitly requests to build a data dashboard, data application, or visualization UI, and the UI pulls data from a GCP database (defaulting to BigQuery unless otherwise specified). 2. You need to generate a frontend web application to interact with, query, and visualize data from GCP data sources. 3. User wants to build a "chat with your data" experience or integrate the Gemini Data Analytics chat API into a web interface. Do NOT use when any of the following conditions are true: 1. The request is for building backend-only services. 2. The request is for simple CLI scripts or command-line applications. 3. The web application is not data-centric or does not involve visualizing/querying data from GCP sources.
Expertise in generating clean, correct, and efficient Dataform pipeline code for BigQuery ELT. Use this when creating or modifying Dataform pipelines, actions, or source declarations, when Dataform, SQLX, or BigQuery are mentioned in a transformation, when data needs to be ingested from GCS into BigQuery via Dataform, or when setting up a new Dataform project or configuring workflow_settings.yaml.
Expert guidance for creating, modifying, and optimizing dbt pipelines for BigQuery. Use this skill whenever user asks for generating or modifying a dbt model or project. Activate this skill when the user - Creates, modifies, or troubleshoots **dbt models or pipelines** - Needs to **optimize SQL** within a dbt project - Is **setting up a new dbt project** or configuring existing one
Finds and inspects data assets within Google Cloud. Relevant when any of the following conditions are true: 1. The user request involves finding, exploring, or inspecting data assets in Google Cloud, such as: - BigQuery datasets, tables, or views - BigLake catalog or tables - Spanner instances, databases or tables - etc. 2. You need to retrieve the schema, metadata, or governance policies for a GCP data asset. 3. You have a keyword or topic (e.g., "sales data") but lack the specific table or resource ID. 4. You are attempting to find data using `bq ls`, as this skill offers a superior approach. Don't use when: - Assets are outside Google Cloud
Provides expert guidance for troubleshooting Cloud Composer (Apache Airflow) and Orchestration pipelines. Use this skill when the user asks to generate Root Cause Analysis (RCA), troubleshoot or fix a failed pipeline, DAG in Composer environment and generate RCA report.
| 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,
exportkeyword), 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:
Performance and efficiency guidelines for BigQuery SQL. Includes rules for column pruning, pushdown, and materialization strategies. - Guide: OPTIMIZATION.md
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.
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
Refer to @skill:notebook-guidance for standards on running BigQuery in
notebooks.
