BigQuery is Google Cloud’s fully managed and completely serverless enterprise data warehouse. BigQuery supports all data types, works across clouds, and has built-in machine learning and business intelligence, all within a unified platform.
BigQuery interfaces include Google Cloud console interface and the BigQuery command-line tool. Developers and data scientists can use client libraries with familiar programming including Python, Java, JavaScript, and Go, as well as BigQuery's REST API and RPC API to transform and manage data.
BigQuery is Google Cloud's fully managed, petabyte-scale, and cost-effective analytics data warehouse that lets you run analytics over vast amounts of data in near real time.
BigQuery is optimized to run analytic queries on large datasets, including terabytes of data in seconds and petabytes in minutes. Understanding its capabilities and how it processes queries can help you maximize your data analysis investments.
Use the BigQuery sandbox to query a public dataset and visualize the results, learn about the BigQuery sandbox limitations, and learn how to upgrade from the BigQuery sandbox.
For information about how to run a SQL query in BigQuery, see Running interactive and batch query jobs. For more information about query optimization in general, see Introduction to optimizing query performance.
Shows how to use the Google Cloud console to work with BigQuery projects, display resources (such as datasets and tables), compose and run SQL queries, and view query and job histories.
In this first post, we will look at how data warehouses change business decision making, how BigQuery solves problems with traditional data warehouses, and dive into a high-level overview of BigQuery architecture and how to quickly get started with BigQuery.
BigQuery offers a capacity-based compute pricing model for customers who need additional capacity or prefer a predictable cost for query workloads rather than the on-demand price (per TiB of data processed).
To power their data clouds, tens of thousands of organizations already choose BigQuery and its integrated AI capabilities. This decade requires AI-native, multimodal, and agentic data-to-AI platforms, with BigQuery leading the way as the autonomous data-to-AI platform.
