Analytics Hub

Since 2010, BigQuery has supported always-live, in-place data sharing within an organization’s security perimeter (intra-organizational sharing) as well as data sharing across boundaries to external organizations, like vendor or partner ecosystems. Looking at usage over a one week period in September 2022, more than 6,000 organizations shared over 275 petabytes of data in BigQuery, not accounting for intra-organizational sharing. Analytics Hub makes the administration of sharing assets across any boundary even easier and more scalable, while retaining access to key capabilities of BigQuery like its built-in ML, real-time, and geospatial analytics.

Create a low-trust environment for you and your partners to collaborate without copying or moving the underlying data right within BigQuery. This allows you to perform privacy-enhancing transformations in BigQuery SQL interfaces and monitor usage to detect privacy threats on shared data. Benefit from BigQuery scale without needing to manage any infrastructure and built-in BI and AI/ML. Explore use cases for data clean rooms.

Exchanges are collections of data and analytics assets designed for sharing. Administrators can easily curate an exchange by managing the dataset listings within the exchange. Rich metadata can help subscribers find the data they're looking for, and even leverage analytics assets associated with that data. Exchanges within Analytics Hub are private by default, but granular roles and permissions can be set easily for you to deliver data at scale to exactly the right audiences. Data publishers can now easily view and manage subscriptions for all their shared datasets. Administrators can now monitor the usage of Analytics Hub through Audit Logging and Information Schema, while enforcing VPC Service Controls to securely share data.

Shared datasets are collections of tables and views in BigQuery defined by a data publisher and make up the unit of cross-project/cross-organizational sharing. Data subscribers get an opaque, read-only, linked dataset inside their project and VPC perimeter that they can combine with their own datasets and connect to solutions from Google Cloud or our partners. For example, a retailer might create a single exchange to share demand forecasts to the thousands of vendors in their supply chain—having joined historical sales data with weather, web clickstream, and Google Trends data in their own BigQuery project—then sharing real-time outputs via Analytics Hub. The publisher can add metadata, track subscribers, and see aggregated usage metrics.

Explore the revamped search experience to browse and quickly find relevant datasets. In addition to easily finding your organization's internal datasets in Analytics Hub, this also includes Google datasets like Google Trends and Earth Engine, commercial datasets from our partners like Crux, and public datasets available in Google Cloud Marketplace.