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.
Enable the BigQuery API:
bash gcloud services enable bigquery.googleapis.com --quiet
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'
Core Concepts [blocked] : Storage types, analytics
workflows, and BigQuery Studio features.
Change History [blocked] : Tracking and querying
incremental table changes using APPENDS and CHANGES.
Continuous Queries [blocked] : Running continuous
SQL statements to analyze incoming data in real time.
CLI Usage [blocked] : Essential bq command-line tool
operations for managing data and jobs.
Client Libraries [blocked] : Using Google Cloud
client libraries for Python, Java, Node.js, and Go.
MCP Usage [blocked] : Using the BigQuery remote MCP server and
Gemini CLI extension.
Infrastructure as Code [blocked] : Terraform examples for
datasets, tables, and reservations.
IAM & Security [blocked] : Roles, permissions, and data
governance best practices.