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Google BigQuery

BigQuery is Google Cloud's serverless, fully managed data warehouse for large-scale analytics. It ranks around #11 in real-world database usage (roughly 6.5% share) and is a favorite for companies that need to run massive SQL queries across billions of rows without managing any servers — you just write SQL and Google handles the infrastructure.

📌 Quick facts
Type: Cloud data warehouse (analytical SQL)
Made by: Google Cloud
License: Proprietary, managed-cloud (pay per data scanned)
Hosting: Fully managed, serverless
Primary use case: Large-scale analytics and business intelligence over huge datasets

Jump to: Example query Getting started Best for

Example query

BigQuery uses Standard SQL, with a few extensions for working with nested and semi-structured data. A typical analytical query aggregates over a huge public or internal dataset:

SELECT
   country,
   COUNT(*) AS total_orders,
   SUM(order_amount) AS revenue
FROM
   `my_project.sales.orders`
WHERE
   order_date BETWEEN '2026-01-01' AND '2026-06-30'
GROUP BY
   country
ORDER BY
   revenue DESC
LIMIT 10; -- scans only the columns/partitions it needs, billed by bytes scanned

Getting started

BigQuery is accessed through the Google Cloud Console, the bq command-line tool, or client libraries — there's nothing to install or provision.

# install the Google Cloud CLI, then authenticate
gcloud auth login

# run a query directly from the terminal
bq query --use_legacy_sql=false 'SELECT COUNT(*) FROM `bigquery-public-data.samples.shakespeare`'

🎯 Best for
Projects that need to crunch huge analytical datasets on demand — dashboards, reporting pipelines, or ad-hoc data science queries — without anyone on the team having to manage a database server.