Businesses increasingly need to collaborate on customer, advertising, transaction and research data without exposing the sensitive records underneath. Brands want to compare audiences with publishers, retailers want to measure advertising effectiveness, and enterprises want to combine datasets with partners while keeping privacy and governance controls intact.
A data clean room is designed for exactly this type of controlled collaboration. Instead of sending a partner a customer database, participating organizations define what data can be used, what questions can be asked, and what outputs are allowed to leave the environment.
Data clean room software provides a governed environment where two or more parties can analyze overlapping datasets while restricting direct access to raw records. In 2026, the category ranges from cloud-native infrastructure such as Snowflake, Databricks and AWS to marketing-oriented providers such as LiveRamp and AppsFlyer.
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Quick summary: This guide compares Snowflake Data Clean Rooms, Databricks Clean Rooms, AWS Clean Rooms, LiveRamp Clean Room, AppsFlyer Data Clean Room, Google Ads Data Hub and Decentriq across privacy architecture, analytics, identity, machine learning, activation, cloud interoperability and multi-party collaboration.
Best Data Clean Room Providers: Quick Comparison
| Data Clean Room | Best For | Key Strength |
|---|---|---|
| Snowflake Data Clean Rooms | Snowflake customers | Native governed collaboration |
| Databricks Clean Rooms | Data science and AI teams | Notebook and lakehouse collaboration |
| AWS Clean Rooms | AWS environments | Multi-party analytics and ML |
| LiveRamp Clean Room | Marketing and identity use cases | Identity resolution and activation |
| AppsFlyer Data Clean Room | Mobile and retail media | Privacy-first marketing collaboration |
| Google Ads Data Hub | Google advertising measurement | Privacy-safe Google ad analysis |
| Decentriq | Regulated and privacy-sensitive use cases | Confidential computing |
7 Best Data Clean Room Software Platforms in 2026
1. Snowflake Data Clean Rooms
Best for: Organizations already using Snowflake
Snowflake Data Clean Rooms are a strong fit for organizations whose analytical data already lives in Snowflake. Collaborators can combine and analyze data under governed rules without giving one another unrestricted access to the underlying records.
Snowflake's current Collaboration API is generally available and defines collaborators, data offerings, templates and permitted analysis runners. This newer collaboration architecture is now the strategic direction for the product.
A particularly important 2026 change is the retirement of Snowflake's legacy Provider and Consumer model. As of October 1, 2026, new legacy clean rooms can no longer be created through the web application; Snowflake is directing customers toward the Collaboration API, with additional legacy retirement milestones continuing into 2027.
Snowflake remains attractive for organizations that want SQL-based analytics, governed multiparty collaboration, audience overlap, activation workflows and clean-room operations close to data already stored in Snowflake.
2. Databricks Clean Rooms
Best for: Data science, AI and lakehouse environments
Databricks Clean Rooms are designed for secure collaboration around analytics, machine learning and enterprise data. The service uses OpenSharing, Unity Catalog and serverless compute so collaborators can work together without direct access to one another's raw data.
Approval-based clean rooms allow collaborators to review notebooks before execution, supporting a no-trust workflow. Databricks also offers packaged clean rooms, where a provider can publish protected notebooks, JARs and data assets while the consumer can run the package without seeing the provider's code or underlying data.
Databricks currently supports up to ten collaborators in a clean room, including the creator. That multi-party model is useful for consortiums, data ecosystems and research scenarios that extend beyond a simple one-provider/one-consumer relationship.
The combination of notebooks, lakehouse governance and protected execution makes Databricks particularly relevant when collaboration requires SQL, Python, data science or AI workflows rather than only marketing measurement.
3. AWS Clean Rooms
Best for: Organizations heavily invested in AWS
AWS Clean Rooms provides a managed collaboration environment where companies and partners can analyze collective datasets without revealing or copying raw information to one another. AWS also supports collaboration with datasets stored in both AWS and Snowflake.
The platform supports SQL, Spark SQL and PySpark analysis, along with AWS Clean Rooms ML for privacy-enhancing machine learning. AWS Entity Resolution can help participating organizations map different identifiers without exposing the underlying source data.
Privacy controls include query restrictions, aggregation controls, differential privacy and cryptographic computing. AWS Clean Rooms ML also supports custom and lookalike modeling so partners can generate predictive insights without exchanging raw training data or proprietary models.
AWS is therefore one of the broadest infrastructure-oriented options for teams that need multi-party analytics, entity resolution, advanced code execution and machine-learning workflows in the same clean-room program.
4. LiveRamp Clean Room
Best for: Marketing, audience collaboration and identity resolution
LiveRamp Clean Room combines governed data collaboration with LiveRamp's identity and marketing ecosystem. It is designed to let partners analyze data together while limiting use to agreed purposes and preventing participants from seeing one another's underlying data.
Identity resolution is a major differentiator. LiveRamp can use RampID-based matching to help collaborators connect customer and audience data even when each party uses different internal identifiers, which is valuable for audience overlap, measurement and activation.
LiveRamp supports multiple architectural patterns, including hybrid and confidential-computing clean rooms plus native-pattern clean rooms for Snowflake, AWS and Google BigQuery. Its documentation also supports cloud data connections spanning AWS, Google Cloud, Azure, Snowflake, BigQuery and Databricks depending on the room type.
This makes LiveRamp especially relevant for advertisers, publishers, retail media networks, agencies and customer-intelligence teams that need clean-room collaboration tightly connected to identity, activation and measurement.
5. AppsFlyer Data Clean Room
Best for: Mobile marketing and retail media networks
AppsFlyer's Data Clean Room is part of its Data Collaboration Platform and is designed around privacy-first advertising and retail-media collaboration. The platform emphasizes secure partnerships without exposing raw customer information.
AppsFlyer currently highlights privacy-enhancing technologies including encryption, anonymization, federated processing and differential privacy. These controls are intended to let participants collaborate while reducing the need to centralize or expose sensitive datasets.
The broader workflow can support audience creation, enrichment, activation and measurement, making it particularly relevant for mobile marketers and retail-media environments where attribution and campaign data need to be combined with first-party business information.
AppsFlyer is a more specialized choice than a general-purpose lakehouse clean room, but that specialization can reduce friction for marketing teams already using AppsFlyer for measurement and attribution.
6. Google Ads Data Hub
Best for: Google advertising measurement
Google Ads Data Hub is a specialized clean-room-style environment for analyzing Google advertising event data together with an advertiser's own information in BigQuery. It is best understood as a privacy-controlled measurement layer inside Google's advertising ecosystem rather than a general-purpose multi-company data collaboration platform.
Queries are subject to privacy checks before results are returned. Google documents static checks, data-access budgets, aggregation thresholds, noise injection and other privacy controls intended to prevent outputs from revealing information about individual users.
Typical use cases include campaign measurement, reach and frequency, audience overlap, conversion analysis and segmentation. The trade-off is scope: Ads Data Hub is most valuable when the primary collaboration objective centers on Google media.
7. Decentriq
Best for: Highly regulated and privacy-sensitive collaboration
Decentriq differentiates itself through confidential computing. Its data clean room platform uses advanced encryption and hardware-backed privacy technology so raw data remains inaccessible to other collaborators and, in confidential-computing scenarios, is protected while being processed.
That architecture can be attractive in financial services, healthcare, pharmaceuticals, insurance, public-sector projects and advertising programs where participants need stronger technical guarantees around how sensitive data is used.
Decentriq also continues to add business-facing features on top of its privacy architecture. On October 1, 2026, it introduced AI Insights, which turns aggregated clean-room results into plain-language summaries while keeping the underlying first-party data inside the confidential-computing environment.
This combination of hardware-backed isolation and easier insight consumption makes Decentriq a notable option for organizations that prioritize technical privacy enforcement rather than relying only on policy and contractual controls.
Data Clean Room Definition
A data clean room is a controlled environment where multiple parties can analyze or combine datasets without exposing each participant's underlying sensitive information.
Instead of sharing unrestricted customer-level records, participants define approved inputs, permitted analyses and output rules. Results may be aggregated reports, audience segments, model outputs or other governed insights rather than raw source data.
How Does a Data Clean Room Work?
A typical data clean room workflow starts with participating organizations connecting the datasets they want to use. Depending on the platform, data may remain in each party's cloud environment, be made available through secure sharing, or be processed inside an isolated clean-room compute environment.
Identity matching may then connect records using email addresses, phone numbers, pseudonymous identifiers, RampIDs or other privacy-preserving identity methods. Data owners define what queries, models or code are allowed to run.
Analysis executes inside the governed environment, and only controlled outputs leave it. Good implementations also maintain audit logs, permissions, aggregation thresholds and data-minimization rules so participants can demonstrate how the collaboration was governed.
Common Data Clean Room Use Cases
Common use cases include audience overlap analysis, campaign measurement, retail-media measurement, customer insights, data enrichment, lookalike modeling, research collaboration, fraud analysis, healthcare research and cross-company analytics.
Advertising remains a major use case because brands, publishers and retailers often need to compare or measure first-party data without directly exchanging customer lists. However, cloud-native clean rooms are increasingly used for broader enterprise analytics and machine-learning collaboration.
Data Clean Room vs Data Warehouse
A data warehouse primarily stores and analyzes information owned or controlled by one organization. A data clean room governs collaboration between multiple parties that do not want to expose raw data to one another.
The two are increasingly connected. Snowflake and Databricks provide clean-room capabilities within broader data platforms, while AWS can read participating datasets from where they already live. In those architectures, the warehouse or lakehouse remains the core analytical environment and the clean room becomes the governed collaboration layer.
Data Clean Room vs Customer Data Platform
A customer data platform unifies customer information for one organization so teams can build profiles, segments and activation workflows. A data clean room is designed for controlled collaboration across organizational boundaries.
The categories can overlap in marketing use cases, especially when CDP audiences are matched with publisher or retail-media data. The important distinction is that a clean room must enforce collaboration rules between parties with separate data ownership and privacy responsibilities.
Privacy-Enhancing Technologies in Data Clean Rooms
Data clean rooms can use different privacy architectures. Common controls include aggregation thresholds, differential privacy, encryption, federated processing, confidential computing, identity tokenization, query allowlists and restricted output schemas.
These mechanisms are not interchangeable. A buyer should understand whether a platform relies mainly on contractual and software policy controls, cloud isolation, cryptographic protection, hardware-backed confidential computing, or a combination of several approaches.
Features to Look for in Data Clean Room Software
Important evaluation criteria include multi-party collaboration, data residency and movement, privacy-enhancing technologies, aggregation thresholds, query controls, identity resolution, SQL and notebook support, machine learning, audience activation, APIs, audit logs, role-based permissions, cloud interoperability and no-code workflows.
Architecture deserves special attention. One platform may require participants to place data inside the same cloud or region, while another may support collaboration where data remains in each company's existing environment. That design choice affects security review, deployment speed, performance and cost.
How to Choose the Best Data Clean Room Provider
Start with where your data already lives. Organizations standardized on Snowflake will naturally find Snowflake Data Clean Rooms easier to adopt, while Databricks users can benefit from Unity Catalog, serverless compute and notebook-based collaboration.
AWS users should consider AWS Clean Rooms when they need multiparty analytics, identity resolution, differential privacy or machine learning. Marketing organizations that need identity matching and activation may prefer LiveRamp, while AppsFlyer fits mobile and retail-media collaboration.
Google Ads Data Hub makes sense when the primary objective is measurement inside Google's advertising ecosystem. Highly regulated organizations should also evaluate confidential-computing architectures such as Decentriq.
During a proof of concept, test a real collaboration rather than only a vendor demo. Confirm where data is stored, who can see it, which queries can run, how identities are matched, what outputs can leave, how audit trails work and whether each participant can independently enforce its own policies.
Data Clean Room Governance Checklist
A clean room does not make data sharing automatically compliant or safe. Organizations still need a legal basis for using the data, data-minimization policies, consent and purpose controls where applicable, strong identity and access management, retention rules, contractual governance and carefully designed outputs.
Snowflake's own clean-room documentation explicitly notes that customers remain responsible for obtaining required rights and consents and for complying with applicable laws. Technology can enforce collaboration boundaries, but governance responsibility remains with the participating organizations.
Final Thoughts
The data clean room market has expanded from a niche advertising technology into a broader enterprise data-collaboration category. Snowflake, Databricks and AWS provide infrastructure-native options, while LiveRamp and AppsFlyer connect clean rooms closely to identity, marketing and activation.
Google Ads Data Hub remains important for Google advertising measurement, while Decentriq offers a confidential-computing-centered alternative for privacy-sensitive collaboration.
The best choice depends on where data already resides, who needs to collaborate, what analyses must run, whether activation is required, and how strongly privacy controls need to be technically enforced. The most effective clean-room program combines the right architecture with clear governance and tightly controlled outputs.
