benefits
BigQuery is designed to help you unify your data, connect it to AI and automate common data tasks, freeing up valuable time for exploration, model building, and strategic analysis, all with leading price-performance.
BigQuery’s architecture is built for petabyte-scale analytics, allowing you to analyse massive datasets quickly and efficiently. It automatically scales to meet your demand, ensuring lightning-fast query performance even with complex queries across trillions of rows of data. This eliminates the need for manual infrastructure provisioning or scaling, allowing you to focus purely on insights.
As a fully managed, serverless data warehouse, BigQuery handles all the underlying infrastructure, maintenance, and upgrades for you. This significantly reduces operational overhead and costs, as you only pay for the data stored and the queries you run. Its pay-as-you-go model makes advanced analytics accessible and affordable, eliminating the need for large upfront investments.
BigQuery is not just a data warehouse; it’s a central hub within Google Cloud’s comprehensive analytics ecosystem. It offers built-in machine learning (BigQuery ML), allowing data analysts to build and operationalise ML models directly using SQL. Seamless integration with tools like Looker for business intelligence, Dataflow for ETL, and Vertex AI for advanced AI capabilities enables end-to-end data pipelines.
uses
Whether you’re in retail, healthcare, or financial services, Google’s purpose-built platforms provide the secure, scalable, and intelligent capabilities you need to succeed in a competitive landscape.
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.
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.
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 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 summarisation, 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.
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.
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