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No infrastructure to manage — scale automatically with pay-per-query pricing.
Query data across Google Cloud Storage, Sheets, and external sources without moving it.
Train and deploy ML models directly in SQL — no need to export data.
Fine-grained access control, encryption, and compliance with enterprise standards.
Enable BigQuery API and set up billing in your Google Cloud Console.
Upload CSV/JSON files or connect to Google Cloud Storage, Sheets, or external sources.
Use BigQuery UI, CLI, or client libraries to run fast, scalable SQL queries.
Use BigQuery ML to train models like linear regression, k-means, or boosted trees in SQL.
Connect to Looker, Data Studio, or export results to dashboards and notebooks.
-- BigQuery ML Linear Regression Example
CREATE OR REPLACE MODEL `project.dataset.model_name`
OPTIONS(model_type='linear_reg') AS
SELECT
age,
income,
purchase_amount
FROM `project.dataset.customer_data`;Analyze campaign ROI and predict conversion rates using BigQuery ML.
Cluster users based on behavior and demographics with k-means models.
Predict future sales using time series models directly in SQL.
Stream and analyze data from Pub/Sub or IoT devices with low latency.
Explore BigQuery’s ecosystem and find the tools, platforms, and docs to accelerate your workflow.
Common questions about BigQuery’s capabilities, usage, and ecosystem.