Overview

Built into BigQuery sharing, formerly Analytics Hub, the service lets parties join and analyze data in place. Shared resources must be BigQuery tables, views, or table-valued functions, so it is a more natural choice for BigQuery-centered partnerships than for organizations seeking a standalone data exchange.

Google identifies campaign planning, measurement and attribution, and activation as primary uses, with applications in retail, financial services, healthcare, and supply chains. Owners manage rooms, contributors publish data, and subscribers query it. That division makes responsibilities clear, though every participant’s workflow depends on the BigQuery sharing setup.

Key features

Query and privacy controls

Contributors can set analysis rules, including aggregation thresholds that limit subscribers to aggregate queries. Rules apply only to views, which constrains how teams can protect shared resources. Query templates let owners and publishers offer predefined queries without exposing the underlying tables or views, a stronger fit when approved-query control matters.

The service automatically blocks subscribers from copying or exporting raw data, and contributors can configure controls on query results. These protections reduce straightforward routes for data removal, but they do not make disclosure impossible: Google warns that sophisticated queries may extract unauthorized data despite analysis rules. Teams handling sensitive data should not rely on rules alone and should consider query templates to constrain analysis.

Scale and deployment

A room can contain up to 100 shared resources; Google directs users to its feedback address to request a higher limit. Clean rooms are available only in BigQuery sharing regions, and listings spanning multiple regions are unsupported. Those boundaries may be manageable for a focused partnership, but they can rule out broad, multi-region catalogs.

Setup requires enabling the Analytics Hub API and assigning IAM roles, including Analytics Hub Admin for owners. Rooms can be managed in the Google Cloud console or created through the API. The cloud deployment and SQL analysis suit teams already equipped to administer BigQuery access; organizations that need a different deployment model should look elsewhere.

Pricing

The plan model is free, but that does not mean querying and storing data have no cost. Contributors pay for data storage, while subscribers pay for compute when they run queries. The cost split follows each party’s role, so collaborators should agree in advance who publishes data and who runs analyses.

Data contributor: Custom pricing; publishes data to a clean room. Contributors are charged for storage.
Data clean room subscriber: Custom pricing; subscribes to and queries clean-room data. Subscribers are charged for compute when they run queries.
Free plan: The service has a free-plan designation, but storage and query compute charges still apply to the relevant parties.

There are no published seat counts or trial terms to factor into this role-based pricing. The key budget question is therefore expected storage and query compute, not a per-seat subscription.

Platforms

BigQuery Data Clean Rooms is a cloud service with web and API access. The console supports room management, while the API supports creation; the product also requires BigQuery sharing and IAM configuration.

Who it's for

This is a strong fit for BigQuery users coordinating data partnerships in campaign planning, measurement, attribution, or activation, especially when data should remain in place and access needs to be separated by role. Retail, financial services, healthcare, and supply chain teams may also find the collaboration model relevant. It is a weaker fit for groups that need multi-region listings, more than 100 resources per room, or privacy guarantees based solely on analysis rules.

Pros and cons

Pros: Parties can join and analyze data without moving underlying records, reducing the need to distribute raw copies.
Pros: Role separation, aggregation rules, query templates, and result controls give contributors several ways to shape subscriber access.
Pros: Storage and compute costs fall to different parties, matching charges to publishing and querying responsibilities.
Cons: Analysis rules are limited to views and may not stop sophisticated extraction, so sensitive collaborations need more than rule configuration.
Cons: The 100-resource ceiling and BigQuery-sharing-region restriction limit larger or geographically distributed setups.
Cons: API enablement and IAM role assignment add administrative work before participants can collaborate.

Alternatives

Amazon Marketing Cloud is another free-plan option with web and API access; consider it when an eligible advertiser wants Amazon’s offering rather than a BigQuery-based clean room.

Placino offers a free tier with 500K datapoints per month, one clean room, and up to three partners, so it may suit a small collaboration with a defined usage cap.

AppsFlyer Data Clean Room has a free plan and supports Android, iOS, web, and API, making it an option when those platform connections matter.

Aggregion DataLabs & CleanRoom offers API, web, and self-hosted platforms; consider it if self-hosting is a requirement.

Anjuna Northstar is a paid, self-hosted alternative for teams seeking that deployment model.

BeeKeeperAI (EscrowAI) is a paid option with web and self-hosted platforms, which may fit teams comparing those deployment choices.

Decentriq Data Clean Rooms is a paid web and API alternative for organizations comparing another clean-room service.

INDICO DataHub 2 has a free trial and a Professional plan at 79.00 USD per month; consider it if a trial and a published monthly plan are priorities.

For broader comparisons, browse Data Clean Room Software, Data Warehouse Software, Query Engine Software, Columnar Databases, SQL Query Tools, and OLAP Databases.

Verdict

Choose BigQuery Data Clean Rooms if your partners already work in BigQuery and need role-based collaboration without moving underlying data. The query templates and configurable controls are useful, but the 100-resource limit, regional constraints, and warning about sophisticated extraction are real reasons to look elsewhere or add stronger query governance.

	BigQuery Data Clean Rooms	Query
Free plan	No	No
Free trial	No	No
Paid from	—	—
Open source	No	No
Platforms	api, Web	api, Web
Free plan	Yes	—
Federated queries	—	Yes
Heterogeneous sources	—	Yes
Deployment	—	cloud
Source connectors	—	5

BigQuery Data Clean Rooms vs Query

BigQuery Data Clean Rooms

Query