Google Cloud BigQuery

Key benefits of Google Cloud BigQuery

Built-in machine learning: BigQuery ML enables data scientists and data analysts to build and operationalize ML models on planet-scale structured, semi-structured, and now unstructured data directly inside BigQuery, using simple SQL—in a fraction of the time. Export BigQuery ML models for online prediction into Vertex AI or your own serving layer. Learn more about the models we currently support.

Analyze and share data across clouds: BigQuery Omni is a fully managed, multicloud analytics solution that allows for cost-effective and secure data analysis across clouds and shares results within a single pane of glass. Within BigQuery Analytics Hub, securely exchange data assets internally and across organizations without data movement and enhance analysis with commercial, public, and Google datasets.

Real-time analytics with built-in query acceleration: BigQuery has built-in capabilities that ingest streaming data and make it immediately available to query, along with native integrations to streaming products like Dataflow. Analyze large datasets interactively with BigQuery BI Engine, an in-memory analysis service that offers sub-second query response time and high concurrency. BI Engine natively integrates with Looker Studio and works with many BI tools, including Connected Sheets.

Unify, manage, and govern all types of data: Query all data types with BigQuery: structured, semi-structured and unstructured. Use BigLake to explore and unify different data types and build advanced models. Centrally discover, manage, monitor, and govern data across data lakes, data warehouses, and data marts with consistent controls with Dataplex, an intelligent data fabric that enables organizations to provide access to trusted data.

Geospatial analysis with BigQuery: BigQuery geospatial uniquely combines the serverless architecture of BigQuery with native support for geospatial analysis, so you can augment your analytics workflows with location intelligence. Simplify your analyses, see spatial data in fresh ways, and unlock entirely new lines of business with support for arbitrary points, lines, polygons, and multi-polygons in common geospatial data formats.

Spreadsheet interface: Connected Sheets allows users to analyze billions of rows of live BigQuery data in Google Sheets without requiring SQL knowledge. Users can apply familiar tools—like pivot tables, charts, and formulas—to easily derive insights from big data. Learn more about Connected Sheets in the getting started guide.

Real-time change data capture and replication: Synchronize data across heterogeneous databases, storage systems, and applications reliably and with minimal latency with Datastream. Datastream integrates with purpose-built and extensible Dataflow templates to pull change streams written to Cloud Storage, and create up-to-date replicated tables in BigQuery for real-time analytics.

Standard SQL: BigQuery supports a standard SQL dialect that is ANSI:2011 compliant, which reduces the need for code rewrites. BigQuery also provides ODBC and JDBC drivers at no cost to ensure your current applications can interact with its powerful engine.

Materialized Views: Accelerate query performance and reduce costs within your environment with BigQuery materialized views. It is easy to set up, effortless to use, and best of all it's real time, allowing you to quickly get answers to your questions.

Petabyte scale: Get great performance on your data, while knowing you can scale seamlessly to store and analyze petabytes to exabytes of data with ease.

Data governance and security: BigQuery's integration with security and privacy services from Google Cloud provides strong security and fine-grained governance controls, down to the column-level and row-level. Rest assured knowing your data is encrypted at rest and in transit by default.

Public datasets: Google Cloud Public Datasets offer a powerful data repository of more than 200 high-demand public datasets from different industries.