BigQuery is Google Cloud's serverless data warehouse. See 2026 pricing, on-demand vs capacity models, key features, pros, cons, and FAQs.
BigQuery is Google Cloud's serverless enterprise data warehouse, first announced in 2010 and generally available since late 2011. It is built on Dremel, the distributed query engine Google originally developed for internal use, and is designed to run interactive SQL analytics over datasets that span gigabytes to petabytes.
Because storage and compute are billed and scaled separately, BigQuery requires no cluster sizing or server management. Teams write standard SQL, and Google Cloud handles distributing the query across its infrastructure, making it a popular choice for centralized analytics warehouses feeding BI tools, data science notebooks, and downstream applications.
BigQuery supports standard ANSI SQL, streaming and batch ingestion, materialized views, and native machine learning through BigQuery ML, which lets analysts train models using SQL syntax alone. BI Engine accelerates dashboard-style queries with in-memory caching.
The platform also offers BigQuery Omni for querying data stored in AWS and Azure without moving it, federated queries against operational databases and Google Sheets, geospatial functions, and time-travel snapshots that let teams recover or query historical table states. Gemini in BigQuery adds AI-assisted SQL generation and natural-language data exploration on top of the core engine.
BigQuery uses consumption-based pricing split between storage and compute. On-demand compute is billed per terabyte of data scanned by a query, with a free monthly allowance; storage is billed per gigabyte per month, with a lower rate applied to tables that haven't been modified in 90 days.
For predictable, high-volume workloads, BigQuery offers capacity-based 'editions' (Standard, Enterprise, Enterprise Plus) with autoscaling compute capacity and optional one- or three-year commitments for discounted rates. AI features such as Gemini in BigQuery are priced separately from core query and storage costs.
BigQuery has a free tier: 1 TiB of on-demand query processing and 10 GB of active storage per month at no cost, with no credit card required to start on Google Cloud's free tier.
BigQuery bills storage and compute separately. On-demand compute is charged per terabyte of data a query scans (around $6.25/TiB), while active storage is billed at roughly $0.02/GB/month. Capacity-based 'editions' with autoscaling compute capacity are also available for predictable, high-volume workloads.
Yes. BigQuery ML lets users create, train, and deploy machine learning models using standard SQL statements directly against data already stored in BigQuery, without exporting it to a separate platform.
Yes, via BigQuery Omni, which runs BigQuery's query engine against data stored in AWS S3 or Azure Blob Storage without requiring the data to be moved into Google Cloud.
Data engineers, analysts, and data scientists at companies of all sizes use BigQuery as a central analytics warehouse for business intelligence, log/event analysis, and ELT pipelines feeding dashboards and ML models.