BigQuery
Serverless, petabyte-scale cloud data warehouse
BigQuery is profiled here as a Data Warehouse tool for engineering teams. Read about features, pricing, and how it compares to related options in the tools directory.
Description
BigQuery is Google Cloud's serverless data warehouse, launched in 2010 and built on Google's Dremel query technology. It separates storage from compute so analytical SQL scales across petabytes without provisioning clusters, billing on the data each query scans or on reserved capacity. BigQuery ML trains models in SQL, and Gemini-based features bring generative AI to data inside the warehouse. Tight integration across Google Cloud and in-warehouse machine learning made it a default analytics layer for many data teams. Streaming ingestion and a BI acceleration engine support dashboards that update as new data lands.
Key Capabilities:
Serverless SQL analytics scaling to petabytes
Separated storage and compute with automatic scaling
BigQuery ML for training models in SQL
BigQuery Omni for querying data across AWS and Azure
Streaming ingestion and built-in BI Engine acceleration
Vector search and Gemini-powered generative AI features
Alternative tools
- Tinybird
Managed ClickHouse for building real-time analytics APIs
- Apache Druid
Real-time analytics database for sub-second queries
- Amazon Redshift
Cloud data warehouse for large-scale analytics on AWS
- Databricks
Lakehouse platform unifying data engineering and AI
- DuckDB
In-process analytical database for fast local queries
- ClickHouse
Open-source columnar database for real-time analytics