BigQuery Basics

BigQuery基础

BigQuery is a serverless, AI-ready data platform that enables high-speed analysis of large datasets using SQL and Python. Its disaggregated architecture separates compute and storage, allowing them to scale independently while providing built-in machine learning, geospatial analysis, and business intelligence capabilities.

BigQuery是一款无服务器、支持AI的数据平台，可使用SQL和Python对大型数据集进行高速分析。其解耦架构将计算与存储分离，允许二者独立扩展，同时提供内置机器学习、地理空间分析和商业智能功能。

Setup and Basic Usage

设置与基础使用

Enable the BigQuery API:

bash

gcloud services enable bigquery.googleapis.com

Create a Dataset:

bash

bq mk --dataset --location=US my_dataset

Create a Table:

Create a file named

schema.json

with your table schema:

json

[
  {
    "name": "name",
    "type": "STRING",
    "mode": "REQUIRED"
  },
  {
    "name": "post_abbr",
    "type": "STRING",
    "mode": "NULLABLE"
  }
]

Then create the table with the

bq

tool:

bash

bq mk --table my_dataset.mytable schema.json

Run a Query:

bash

bq query --use_legacy_sql=false \
'SELECT name FROM `bigquery-public-data.usa_names.usa_1910_2013` \
WHERE state = "TX" LIMIT 10'

启用BigQuery API：

bash

gcloud services enable bigquery.googleapis.com

创建数据集：

bash

bq mk --dataset --location=US my_dataset

创建表：

创建一个名为

schema.json

的文件，定义您的表结构：

json

[
  {
    "name": "name",
    "type": "STRING",
    "mode": "REQUIRED"
  },
  {
    "name": "post_abbr",
    "type": "STRING",
    "mode": "NULLABLE"
  }
]

然后使用

bq

工具创建表：

bash

bq mk --table my_dataset.mytable schema.json

运行查询：

bash

bq query --use_legacy_sql=false \
'SELECT name FROM `bigquery-public-data.usa_names.usa_1910_2013` \
WHERE state = "TX" LIMIT 10'

Reference Directory

参考目录

Core Concepts: Storage types, analytics workflows, and BigQuery Studio features.
CLI Usage: Essential
```
bq
```
command-line tool operations for managing data and jobs.
Client Libraries: Using Google Cloud client libraries for Python, Java, Node.js, and Go.
MCP Usage: Using the BigQuery remote MCP server and Gemini CLI extension.
Infrastructure as Code: Terraform examples for datasets, tables, and reservations.
IAM & Security: Roles, permissions, and data governance best practices.

If you need product information not found in these references, use the Developer Knowledge MCP server
search_documents
tool.

核心概念：存储类型、分析工作流和BigQuery Studio功能。
CLI使用：用于管理数据和作业的
```
bq
```
命令行工具核心操作。
客户端库：使用Google Cloud客户端库进行Python、Java、Node.js和Go开发。
MCP使用：使用BigQuery远程MCP服务器和Gemini CLI扩展。
基础设施即代码：用于数据集、表和预留资源的Terraform示例。
IAM与安全：角色、权限和数据治理最佳实践。

如果您在这些参考资料中找不到所需的产品信息，请使用开发者知识MCP服务器的
search_documents
工具。

bigquery-basics

Original

Translation

BigQuery Basics

BigQuery基础

Setup and Basic Usage

设置与基础使用

Reference Directory

参考目录

Related Skills

相关技能