Go from data to AI to action faster with the Autonomous Data and AI Platform

BigQuery is the Autonomous Data to AI Platform for organisations seeking to unify their multimodal data, accelerate innovation with AI, and simplify data analytics across all users in the enterprise.

Features

Power your data agents with Gemini in BigQuery

Gemini in BigQuery provides AI-powered assistive and collaboration features, including code assist, visual data preparation, and intelligent recommendations that help enhance productivity and optimise costs.

BigQuery provides a single, unified workspace that includes a SQL, a notebook, and a NL-based canvas interface for data practitioners of various coding skills to simplify analytics workflows from data ingestion and preparation to data exploration and visualisation to ML model creation and use.

Bring multiple engines to a single copy of data

Serverless Apache Spark is available directly in BigQuery. You can write and execute Spark in BigQuery Studio without exporting data or managing infrastructure. BigQuery metastore provides shared runtime metadata for SQL and open source engines for a unified set of security and governance controls across all engines and storage types. By bringing multiple engines, including SQL, Spark, and Python, to a single copy of data and metadata, you can break down data silos and increase efficiency.

BigQuery provides a single, unified workspace that includes a SQL, a notebook, and a NL-based canvas interface for data practitioners of various coding skills to simplify analytics workflows from data ingestion and preparation to data exploration and visualisation to ML model creation and use.

Manage all data types and open formats

Use BigQuery to manage all data types across clouds, structured and unstructured, with fine-grained access controls. Support for open table formats gives you the flexibility to use existing open source and legacy tools while getting the benefits of an integrated data platform. BigLake, BigQuery’s storage engine, lets you have a common way to work with data and makes open formats like Apache Iceberg, Delta, and Hudi. Read new research on BigQuery’s Evolution toward a Multi-Cloud Lakehouse.

BigQuery provides a single, unified workspace that includes a SQL, a notebook, and a NL-based canvas interface for data practitioners of various coding skills to simplify analytics workflows from data ingestion and preparation to data exploration and visualisation to ML model creation and use.

Built-in machine learning

BigQuery ML provides built-in capabilities to create and run ML models for your BigQuery data. You can leverage a broad range of models for predictions, and access the latest Gemini models to derive insights from all data types and unlock generative AI tasks, such as text summarization, text generation, multimodal embeddings, and vector search. It increases the model development speed by directly bringing ML to your data and eliminating the need to move data from BigQuery.

BigQuery provides a single, unified workspace that includes a SQL, a notebook, and a NL-based canvas interface for data practitioners of various coding skills to simplify analytics workflows from data ingestion and preparation to data exploration and visualisation to ML model creation and use.

Built-in data governance

Data governance is built into BigQuery, including full integration of Dataplex capabilities, such as a unified metadata catalog, data quality, lineage, and profiling. Customers can use rich AI-driven metadata search and discovery capabilities for assets, including dataset schemas, notebooks and reports, public and commercial dataset listings, and more. BigQuery users can also use governance rules to manage policies on BigQuery object tables.

BigQuery provides a single, unified workspace that includes a SQL, a notebook, and a NL-based canvas interface for data practitioners of various coding skills to simplify analytics workflows from data ingestion and preparation to data exploration and visualisation to ML model creation and use.