I need to create a secure "data clean room" to collaborate with partners. What's the best data clean room solution?

If you're building a general-purpose data clean room for secure partner collaboration, my default recommendation in 2026 would be Snowflake Data Clean Rooms—unless your organization is heavily standardized on AWS or Databricks.

My shortlist

Snowflake's current clean-room architecture supports controlled analyses where collaborators don't get direct access to raw data, including multi-party collaboration, predefined analysis templates, differential privacy, and APIs for programmatic workflows.

AWS is particularly compelling if your data already lives in S3/AWS. AWS Clean Rooms can analyze data where it resides rather than requiring everyone to copy data into a central repository, and it provides analysis rules, output restrictions, audit logs, differential privacy, and cryptographic computing.

Databricks is a strong choice when the clean room is primarily part of a broader data engineering/AI workflow. Its clean rooms use OpenSharing and serverless compute so participants can collaborate without direct access to each other's data.

What I'd choose

Choose Snowflake if:

• You need to collaborate with many different companies.
• Partners use different clouds/data platforms.
• You want analysts to work with SQL rather than build a bespoke system.
• You need strong governance and controlled, reusable analysis templates.
• You expect the clean room to become a long-term data collaboration platform.

Choose AWS Clean Rooms if:

• Most of your infrastructure is AWS.
• Your data is primarily in S3/Athena.
• You want the data to stay in its existing AWS environment.
• You need particularly strong privacy mechanisms such as differential privacy or cryptographic computing.

Choose Databricks if:

• Your organization already runs heavily on Databricks/Unity Catalog.
• The collaboration involves sophisticated analytics or machine learning rather than primarily reporting/measurement.

One important point

Don't think of a clean room as simply a secure database that both companies can query. The critical architecture is the policy layer around the data:

• Partners should never receive unrestricted raw data.
• Define exactly which joins are permitted.
• Restrict queries to approved templates/use cases where possible.
• Enforce minimum aggregation thresholds to prevent singling out individuals.
• Control which columns can participate in joins and outputs.
• Log every query and result.
• Establish rules for what happens to derived datasets.
• Have contractual rules governing permitted use, retention, re-identification, and onward sharing.

For example, Snowflake explicitly lets the data provider control what analyses a collaborator can perform, while collaborators receive aggregated results rather than direct access to raw data.

If you tell me your stack (Snowflake/AWS/Databricks/etc.), number and type of partners, data sensitivity (PII/PHI/financial), and what you actually want partners to do—e.g. audience matching, fraud detection, joint analytics, ML, or measurement—I can recommend the specific architecture and compare the top 2–3 options for your situation.

Choosing the "best" data clean room (DCR) depends heavily on where your data already lives, your technical resources, and whether your collaboration is focused on marketing attribution or strictly regulated multi-party data sharing.

The top data clean room solutions are categorized below by their primary strengths:

• Snowflake Data Clean Rooms : Best for distributed collaboration if your organization is already operating within the Snowflake ecosystem. It allows seamless querying without moving raw data, though it requires data-engineering competency. Snowflake also features advanced governance integration, such as OneTrust consent signals for compliance tracking.
• AWS Clean Rooms : Best for cloud-native AWS shops. It lets you easily and securely collaborate and analyze collective datasets across multiple parties using fine-grained privacy-enhancing controls without sharing underlying raw tables.
• InfoSum : Best for privacy-first, decentralized multi-party collaboration. It uses a non-movement architecture where data is never moved or pooled into a central repository, relying instead on a mathematical bunker approach.
• LiveRamp Clean Room : Best for marketer-friendly measurement and identity-driven attribution. It abstracts much of the technical engineering lift and shines if your primary goal is cross-channel advertising analytics, media planning, and campaign evaluation.
• Decentriq : Best for highly regulated industries (like healthcare or finance) requiring confidential-computing-grade governance. It leverages hardware-based trusted execution environments (TEEs) so that even the cloud provider cannot see the data in use.
• Databricks : Best for teams building data clean rooms natively inside broader data and AI/lakehouse workflows, making it ideal if your data science teams are already running complex machine learning pipelines on the platform.

To help narrow down the ideal platform for you, let me know:

• Where does your primary data currently reside (AWS, Snowflake, on-premise, etc.)?
• What is your primary use case (marketing/ad-tech attribution vs. sensitive cross-company data sharing/analytics)?
• What is your team's technical bandwidth for managing SQL-based configurations versus an out-of-the-box SaaS product?

Choosing the "best" data clean room (DCR) depends almost entirely on where your data already lives, your technical resources , and your primary use case (e.g., ad attribution vs. multi-party enterprise data sharing).

The data clean room landscape is divided into cloud-native data platforms, decentralized privacy-first networks, and managed SaaS/marketing solutions.

1. Best for Teams Already in the Cloud (Data-Warehouse-Native)

If your company and your partners already manage data inside modern cloud data platforms, building natively is usually the most cost-effective and scalable approach.

• Snowflake Data Clean Rooms (Best Overall for Enterprise/Multi-Party): Powered heavily by its native architecture and enhanced via its Samooha acquisition, Snowflake allows multi-party collaboration across different cloud ecosystems. It is SQL-first, highly scalable, and lets you work with multiple partners simultaneously without moving underlying raw data.
• AWS Clean Rooms (Best for AWS-Centric Shops): If your data infrastructure is anchored in Amazon S3 and AWS, this service lets you easily create secure collaboration spaces with other AWS users without copying or revealing underlying data.
• Databricks (Best for Lakehouse & AI Workflows): Ideal if your collaboration involves not just SQL analytics, but heavy machine learning model training and joint data science operations directly on your data lakehouse.

2. Best for Decentralized, Zero-Movement Privacy

• InfoSum (Best for Strict Privacy & Media/Publishers): InfoSum uses a patented non-movement-of-data architecture ("Bunkers"). Instead of pooling data into a shared cloud environment, computation happens locally on each participant’s infrastructure. It is a gold standard for heavily regulated sectors (like healthcare or finance) or strict GDPR environments where raw data cannot cross organizational boundaries.

3. Best for Marketer-Friendly & Managed Solutions

If your teams lack heavy data engineering resources or you need turnkey audience matching and media attribution out-of-the-box:

• LiveRamp Clean Room (Best for Identity Resolution & Media Activation): The market leader in identity-driven attribution. It connects smoothly to an enormous global network of media publishers and data partners, making it ideal for brand-to-publisher media measurement.
• Decentriq (Best for Regulated Industries via Confidential Computing): Relies on hardware-based confidential computing (secure enclaves) to guarantee that even the cloud provider cannot see the data being processed.

How to Pick the Right One

• Choose Snowflake or AWS if you have data engineers on staff, already use these data warehouses, and want to avoid paying high external SaaS markup fees.
• Choose InfoSum if your partners are hesitant to upload/sync data to a centralized cloud warehouse and require decentralized, absolute zero-data-leakage architecture.
• Choose LiveRamp if your primary goal is advertising measurement, media planning, and cross-publisher audience activation without building custom SQL pipelines.

To help narrow down the ideal platform, let me know:

• Where does your primary data currently reside (Snowflake, AWS, S3, Google Cloud, or on-prem)?
• Is your collaboration primarily for advertising/marketing attribution or general business/product data sharing?
• Do your partners have technical engineering teams or do you need a turnkey SaaS interface?

If you mean a general-purpose enterprise data clean room—where two or more companies can collaborate on sensitive data without exposing their raw datasets—my default recommendation in 2026 is:

My shortlist

What I'd pick

If you're already on Snowflake → Snowflake Data Clean Rooms.

If you're primarily on AWS or need to collaborate with partners across different data platforms → AWS Clean Rooms. Its ability to work with data where it resides, including AWS and Snowflake, makes it particularly compelling for cross-company collaboration.

If you're already a Databricks shop → Databricks Clean Rooms. The integration with Unity Catalog and its existing governance model can make this substantially simpler than introducing another platform.

I would not build a clean room from scratch unless you have unusually specialized requirements. The hard part isn't creating an isolated database; it's enforcing what analyses are permitted, what outputs can leave the room, who can run them, preventing re-identification, auditing activity, and handling multiple parties. The managed offerings have those controls built in. AWS, for example, provides analysis rules, output constraints, differential privacy, cryptographic computing, and analysis logs.

One important Snowflake caveat

If you choose Snowflake, use the new Collaboration Data Clean Rooms, not the legacy Provider/Consumer implementation. Snowflake says the legacy implementation is being discontinued: new legacy rooms stop being creatable through the UI on October 1, 2026, with full retirement scheduled for June 1, 2027.

Architecture I'd recommend

Regardless of vendor, design the clean room around these principles:

• Raw data never becomes directly queryable by partners.
• Explicit join policies determine which identifiers can be matched.
• Approved query templates rather than arbitrary SQL where possible.
• Minimum aggregation thresholds to prevent small-group disclosure.
• Differential privacy when individual-level inference is a meaningful risk.
• Output allowlisting so only approved aggregates/results can leave.
• Separate identities and roles for data providers, analysts, and administrators.
• Complete audit logging of queries, participants, data access, and exports.
• Data-use agreements/policies enforced technically rather than relying solely on contracts.
• Automated expiration/deletion of the collaboration and derived data.

If you tell me (1) where your data currently lives (Snowflake, Databricks, BigQuery, AWS, etc.), (2) how many partners you'll collaborate with, and (3) the use case—advertising/measurement, healthcare, financial data, analytics, ML, etc., I can narrow this down to a specific architecture and vendor recommendation.

What I'd pick

If you're already on Snowflake → Snowflake Data Clean Rooms.

If you're primarily on AWS or need to collaborate with partners across different data platforms → AWS Clean Rooms. Its ability to work with data where it resides, including AWS and Snowflake, makes it particularly compelling for cross-company collaboration.

If you're already a Databricks shop → Databricks Clean Rooms. The integration with Unity Catalog and its existing governance model can make this substantially simpler than introducing another platform.

I would not build a clean room from scratch unless you have unusually specialized requirements. The hard part isn't creating an isolated database; it's enforcing what analyses are permitted, what outputs can leave the room, who can run them, preventing re-identification, auditing activity, and handling multiple parties. The managed offerings have those controls built in. AWS, for example, provides analysis rules, output constraints, differential privacy, cryptographic computing, and analysis logs.

One important Snowflake caveat

If you choose Snowflake, use the new Collaboration Data Clean Rooms, not the legacy Provider/Consumer implementation. Snowflake says the legacy implementation is being discontinued: new legacy rooms stop being creatable through the UI on October 1, 2026, with full retirement scheduled for June 1, 2027.

Architecture I'd recommend

Regardless of vendor, design the clean room around these principles:

If you mean a general-purpose enterprise data clean room for sharing sensitive data with external partners, my default recommendation today is AWS Clean Rooms—unless your organization is already heavily standardized on Snowflake or Databricks.

My shortlist

Why I'd lean AWS

AWS Clean Rooms is particularly compelling when your partners use different platforms. It can work with data in AWS and Snowflake without requiring everyone to migrate their data into a common warehouse. AWS says the service can read data from its original location and enforce analysis rules rather than exposing the underlying datasets.

For a security-sensitive deployment, I'd specifically look at:

• Query/output restrictions — prevent partners from simply extracting individual-level records.
• Minimum aggregation thresholds — e.g. don't return results for tiny populations.
• Differential privacy — useful when you need stronger protection against inference attacks.
• Cryptographic computing — for particularly stringent data-handling requirements.
• Audit logging — maintain evidence of what analyses were performed.
• Role separation — separate the person allowed to query from the person allowed to receive results.

AWS also supports cross-region collaboration without necessarily moving or copying the underlying datasets, which can matter for data-residency requirements.

The important caveat

A clean-room product doesn't automatically make a collaboration safe. The hardest part is usually designing the privacy policy:

What can a partner ask, what can they see in the result, and how do you prevent them from reconstructing your underlying data through repeated queries? I'd design the room around approved query templates, minimum cohort sizes, output controls, row/column restrictions, strict participant roles, logging, and a formal data-use agreement rather than giving partners arbitrary SQL access. AWS itself emphasizes that customers are responsible for configuring appropriate analysis restrictions.

If you tell me your architecture—for example, Snowflake/AWS/Databricks, number of partners, type of data, whether partners need SQL vs. ML, and whether this is advertising/measurement or general B2B data sharing—I can recommend the best option and sketch the secure architecture.

Choosing the "best" data clean room (DCR) depends heavily on where your data already lives, your technical capabilities, and whether your collaboration is focused on marketing attribution or deep multi-party data science.

Top data clean room solutions categorized by their core strengths include:

• Snowflake Data Clean Rooms : Best for distributed cross-platform collaboration . If you and your partners already operate within the Snowflake AI Data Cloud , this native option makes it seamless to collaborate without moving or copying underlying raw data files.
• AWS Clean Rooms : Best for cloud-native AWS environments . It allows you to easily configure secure analysis workspaces with partners across different AWS accounts without revealing underlying table rows.
• Databricks Clean Rooms : Best for lakehouse-native data and AI workflows . Leveraging Unity Catalog, it allows secure notebook-based sharing and governance, making it powerful if you are combining data collaboration with machine learning or AI models.
• InfoSum : Best for privacy-first, decentralized matching . It utilizes a non-movement architecture (using a "bunker" approach) where raw data never leaves either party's control, which is ideal for sensitive consumer data matching.
• LiveRamp Clean Room : Best for marketer-friendly identity resolution and attribution . It excels if your primary use case is media planning, advertising measurement, and cross-channel campaign analysis.
• Decentriq : Best for highly regulated industries (like healthcare or finance) requiring strict confidential computing guarantees and robust hardware-isolated governance.

To help narrow down the ideal platform, tell me:

• Where does your primary data currently live (AWS, Snowflake, Azure, or on-prem)?
• What is the primary use case (advertising/marketing attribution vs. joint data science/AI)?
• Will your partners be using the same platform , or do you need cross-cloud/agnostic interoperability?

There is no single "best" data clean room (DCR) because the right choice depends heavily on your existing tech stack, where your data lives , and your primary use case (e.g., ad attribution vs. deep multi-party data science).

The top data clean room solutions categorized by their strengths include:

• Snowflake Data Clean Rooms
  • Best for: Teams already utilizing the Snowflake AI Data Cloud.
  • Why it shines: It allows cross-cloud and cross-region collaboration without moving raw data. It features low IT overhead, easy governance, and native integration with tools like OneTrust for privacy consent signals.
• AWS Clean Rooms
  • Best for: AWS-centric data architectures.
  • Why it shines: Lets you easily analyze and collaborate on collective datasets stored in Amazon S3 without sharing or copying the underlying data out of your environment.
• InfoSum
  • Best for: Decentralized, privacy-first multi-party collaboration.
  • Why it shines: Uses a non-movement architecture (using a "bunker" approach) where data is never centralized or shared, making it heavily privacy-compliant and ideal for media/brand partnerships.
• LiveRamp Clean Room
  • Best for: Marketer-friendly identity resolution, attribution, and omnichannel media planning.
  • Why it shines: Highly dominant in the ad-tech and retail media network space, seamlessly connecting first-party customer data to robust identity graphs.
• Decentriq
  • Best for: Highly regulated industries (healthcare, pharma, finance).
  • Why it shines: Relies on confidential computing (data encrypted even while being processed in memory via specialized hardware), offering extreme security guarantees.
• Databricks
  • Best for: Lakehouse-native workflows and advanced machine learning modeling across partners.

To help narrow down which solution fits your needs, tell me:

• Where is your current data stored (Snowflake, AWS, Google Cloud, or on-prem)?
• Are you collaborating primarily for marketing/advertising attribution or deep data science/analytics?

If you’re building a secure data clean room for ongoing collaboration with external partners, I’d shortlist three serious options:

My default recommendation: AWS Clean Rooms

If I didn't know anything else about your architecture, AWS Clean Rooms would be my starting point. It lets multiple organizations analyze combined datasets without exposing the underlying records, while providing configurable analysis rules, differential privacy, audit logging, and cryptographic computing. It can also work with data remaining in AWS or Snowflake rather than requiring you to create copies of everything.

The particularly interesting security feature is cryptographic computing: AWS provides a mechanism where sensitive data can remain encrypted during the collaborative computation, rather than merely being encrypted at rest and in transit.

When I'd choose Snowflake instead

Choose Snowflake Data Clean Rooms if your organization and partners already live in Snowflake. Its clean rooms provide controlled analysis where collaborators can receive approved/aggregated results without directly querying raw data, and Snowflake supports collaboration across AWS, Azure, and Google Cloud.

That's probably the lowest-friction option for a Snowflake-centric data organization.

When Databricks wins

Choose Databricks Clean Rooms if the collaboration involves substantial data science, analytics, or ML, particularly if you're already using Unity Catalog. Databricks' current implementation uses serverless compute and OpenSharing, and supports approval-based and packaged clean-room models.

More important than the vendor: design the clean room correctly

I would architect it so that:

• Neither party gets raw access to the other's data.
• Data owners explicitly control which columns/tables are exposed.
• Partners can execute only pre-approved query templates or narrowly constrained SQL.
• Results have minimum aggregation thresholds to prevent singling out individuals.
• Consider differential privacy when individual-level re-identification is a concern. AWS, for example, provides managed differential privacy that adds calibrated noise to query results.
• Keep an immutable audit trail of queries, users, outputs, and approvals.
• Separate the clean-room environment from your normal production analytics environment.
• Establish contractual rules for what outputs partners may retain or combine with other data.
• For highly sensitive data, consider cryptographic techniques rather than relying solely on access controls.

Bottom line: AWS Clean Rooms is my general-purpose pick; Snowflake Data Clean Rooms is my pick for a Snowflake shop; Databricks Clean Rooms is my pick for an AI/ML-heavy Databricks shop.

If you tell me (1) your cloud/data warehouse, (2) what kind of partners you're collaborating with, (3) what data you're sharing, and (4) whether the goal is analytics, advertising, ML, or something else, I can recommend the specific architecture and compare the three on security, cost, interoperability, governance, and implementation effort.

When I'd choose Snowflake instead

Choose Snowflake Data Clean Rooms if your organization and partners already live in Snowflake. Its clean rooms provide controlled analysis where collaborators can receive approved/aggregated results without directly querying raw data, and Snowflake supports collaboration across AWS, Azure, and Google Cloud.

That's probably the lowest-friction option for a Snowflake-centric data organization.

When Databricks wins

Choose Databricks Clean Rooms if the collaboration involves substantial data science, analytics, or ML, particularly if you're already using Unity Catalog. Databricks' current implementation uses serverless compute and OpenSharing, and supports approval-based and packaged clean-room models.

More important than the vendor: design the clean room correctly

Choosing the "best" data clean room (DCR) solution depends entirely on your existing technology stack, where your data lives, and whether your collaboration is primarily technical or marketing-focused . Because raw data never leaves either party's control, the leading options fit distinct enterprise use cases:

Top Data Clean Room Solutions

• Snowflake Data Clean Rooms : Best for distributed collaboration across multiple clouds and organizations, especially if you or your partners are already embedded in the Snowflake ecosystem. It offers both a no-code UI for business/marketing users and developer APIs native to Snowsight for technical teams.
• AWS Clean Rooms : Best if your infrastructure is natively hosted on Amazon Web Services (AWS). It allows you to easily configure and collaborate on AWS data without having to copy or move underlying data out of your data store.
• Databricks Clean Rooms : Best for data and AI-heavy workflows. Leveraging Delta Sharing and Unity Catalog, it allows multi-party analytics directly inside the data lakehouse without raw data exposure.
• InfoSum : Best for decentralized, privacy-first multi-party collaboration. Instead of moving data into a centralized warehouse, InfoSum uses a non-movement architecture ("pioneering") to match and analyze data sets safely.
• LiveRamp Clean Room : Best for marketing attribution, customer identity resolution, and omnichannel campaign measurement across media networks and publishers.
• Decentriq : Best for highly regulated industries (like healthcare or finance) that require confidential-computing-grade hardware isolation and strict governance.

Key Questions to Narrow Down Your Choice

To figure out which platform fits your partnership model best, consider the following:

If you can share:

• Where your primary data is stored (AWS, Snowflake, Azure, etc.)
• The technical skill level of the teams using the clean room
• Your primary industry or use case (e.g., ad tech, healthcare, supply chain)

I can give you a tailored recommendation and implementation approach.

If you mean a general-purpose, secure environment where your company and external partners can analyze overlapping datasets without exposing the underlying records, I’d shortlist AWS Clean Rooms, Snowflake Data Clean Rooms, and Databricks Clean Rooms. The best choice depends heavily on where your data already lives.

My default recommendation: AWS Clean Rooms

aws.amazon.com is probably the strongest general-purpose choice if security/privacy controls are your top priority and you operate primarily on AWS.

It provides:

• No need to hand raw datasets to partners; data can remain in its original AWS or Snowflake location.
• Fine-grained query and output restrictions, including aggregation thresholds.
• Differential privacy to reduce re-identification risk.
• Cryptographic computing for particularly sensitive workloads.
• Audit/analysis logging.
• SQL, Spark/PySpark, and privacy-enhanced ML capabilities.
• Multiple participants in a collaboration, although AWS currently documents a limit of five participants per collaboration.

That combination makes it particularly attractive for things like customer matching, marketing measurement, fraud analytics, research, and joint ML.

When I'd choose something else

snowflake.com is particularly compelling if you're already a Snowflake shop. It lets collaborators analyze data inside the clean-room environment while preventing direct querying of raw data, and Snowflake supports collaboration across AWS, Azure, and Google Cloud.

One important distinction

A data clean room isn't automatically a privacy solution. The hard part is designing what partners are allowed to learn.

For example, you generally want to prevent a partner from running queries like:

"Give me every customer who appears in both datasets." Instead, you might permit:

"Give me the aggregate conversion rate for groups containing at least 1,000 people." AWS explicitly recommends assessing re-identification risk, establishing agreements with collaborators, and using additional audit mechanisms rather than treating the clean room itself as sufficient protection.

What I'd build

For a serious production implementation, I'd use this architecture:

Your data → governed cloud storage/warehouse → clean room → approved queries/ML → aggregated results → partner

with:

• No raw-data exports
• Explicit partner allowlists
• Minimum cohort/aggregation thresholds
• Query templates rather than unrestricted SQL where possible
• Differential privacy for sensitive aggregate analysis
• Encryption with customer-controlled keys where appropriate
• Complete query/audit logging
• Data-use agreements defining permitted analyses
• Automated monitoring for differencing/re-identification attacks
• Separate clean rooms/collaborations for materially different partners or purposes

If you tell me where your data currently lives (AWS/S3, Snowflake, Databricks, Azure, BigQuery, etc.), the type of partners you're collaborating with, and what you need them to calculate, I can recommend a specific architecture and compare the top 2–3 options—including security, privacy, interoperability, cost, and implementation complexity.

snowflake.com is particularly compelling if you're already a Snowflake shop. It lets collaborators analyze data inside the clean-room environment while preventing direct querying of raw data, and Snowflake supports collaboration across AWS, Azure, and Google Cloud.

One important distinction

A data clean room isn't automatically a privacy solution. The hard part is designing what partners are allowed to learn.

For example, you generally want to prevent a partner from running queries like:

"Give me every customer who appears in both datasets." Instead, you might permit:

"Give me the aggregate conversion rate for groups containing at least 1,000 people." AWS explicitly recommends assessing re-identification risk, establishing agreements with collaborators, and using additional audit mechanisms rather than treating the clean room itself as sufficient protection.

What I'd build

For a serious production implementation, I'd use this architecture:

Your data → governed cloud storage/warehouse → clean room → approved queries/ML → aggregated results → partner

with: