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

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:

If you mean a general-purpose clean room for securely collaborating with external partners, my default recommendation in 2026 is AWS Clean Rooms—unless your organization is already deeply standardized on Snowflake.

My shortlist

Why I'd lean AWS Clean Rooms

The important distinction is that a clean room shouldn't merely be a separate database with restricted access. You want technical enforcement of what partners can learn.

AWS Clean Rooms supports:

• No raw-data sharing: participants can keep datasets in their own environments rather than copying them into a common repository. It can work with data in AWS and Snowflake.
• Query restrictions: you can constrain what analyses partners are permitted to perform and impose minimum aggregation thresholds.
• Differential privacy: useful when even aggregate results could potentially reveal individual participants.
• Cryptographic computing: data can remain encrypted during collaborative computation for particularly sensitive workloads.
• Separation of duties: for example, one participant can run queries while another receives the results.
• ML collaboration: partners can train/use models without exposing the underlying datasets or, in some configurations, the proprietary model.

Snowflake is arguably the better answer if Snowflake is already your company's data platform. Its clean rooms let the data owner control which analyses are available, while collaborators receive controlled results rather than direct raw-data access.

One important Snowflake detail: its legacy Provider/Consumer clean rooms are being retired, with the newer Collaboration API becoming the path forward.

The architecture I'd recommend

Regardless of vendor, I'd design the clean room around this model:

Partner A data → Partner-controlled clean-room environment ← Partner B data ↓ Approved queries / analysis templates ↓ Privacy controls + minimum cohort thresholds + auditing ↓ Only aggregated/approved results leave the clean room

Avoid giving partners arbitrary SQL against sensitive tables. Ideally, partners can execute pre-approved analysis templates, with controls that prevent small-cell results, row-level extraction, repeated differencing attacks, and unauthorized joins.

Also separate data access, query execution, and result receipt wherever possible.

Bottom line: If you haven't already committed to a particular data platform, I'd start by evaluating AWS Clean Rooms. If you're a Snowflake-centric organization, evaluate Snowflake Data Clean Rooms first. The choice should ultimately be driven less by the UI and more by your data residency, identity/matching, privacy model, partner ecosystems, workloads, and compliance requirements.

If you tell me (1) what kind of data you're sharing, (2) how many partners, (3) whether you're on AWS/Azure/GCP/Snowflake, and (4) what partners need to do with the data, I can narrow this to a specific architecture and vendor choice.

The best data clean room (DCR) solution depends on your existing tech stack and whether your focus is marketing or multi-cloud data engineering, with top industry options including Snowflake Data Clean Rooms, Databricks Clean Rooms , and LiveRamp.

Top Data Clean Room Solutions

How to Choose the Right Solution

• If you are already on Snowflake: Use Snowflake Data Clean Rooms because it lets you collaborate directly inside your data warehouse without copying raw records.
• If you rely on heavy data science/ML: Choose Databricks Clean Rooms to run custom Python notebooks and advanced machine learning models safely across partner boundaries.
• If your main goal is media and advertising: Go with LiveRamp to easily connect with external publishers, agencies, and retail networks.

To help narrow down the best choice, tell me:

• What cloud platform or data warehouse (Snowflake, AWS, Azure, GCP) do you currently use?
• Is your primary use case marketing/adtech or general data science/analytics?

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

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

1. • Best for: Distributed, cross-cloud collaboration and enterprises already embedded in the Snowflake ecosystem.
   • Why choose it: It operates as a neutral platform that lets you collaborate across AWS, Azure, and Google Cloud without moving or copying raw underlying data. It supports complex SQL-based queries and multi-party collaboration, though it requires solid data engineering capabilities.
2. AWS Clean Rooms
   • Best for: Teams deeply embedded in Amazon Web Services (AWS).
   • Why choose it: Allows you to easily configure a secure clean room in minutes using your S3 data buckets. It offers granular privacy controls and analytical tools with minimal data movement, making it a favorite for media and finance companies working within the AWS perimeter.
3. Databricks Clean Rooms
   • Best for: Lakehouse-native workflows and advanced data science/AI modeling.
   • Why choose it: Built on Delta Sharing and Unity Catalog, it allows collaborators to safely run live notebooks and queries against shared lake tables without exposing raw rows, backed by strict serverless governance and comprehensive audit logging.
4. LiveRamp Clean Room
   • Best for: Marketer-friendly customer attribution, identity resolution, and media activation.
   • Why choose it: Excellent if your main goal is connecting first-party customer data with media publishers, agencies, and walled gardens. It abstracts much of the heavy engineering lift into identity-driven insights.
5. InfoSum
   • Best for: Decentralized, privacy-first multi-party data matching.
   • Why choose it: Uses a non-movement architecture where data is never pooled or shared; instead, data is virtualized in separate containers, making it ideal for sensitive brand-to-brand partnerships.
6. Walled-Garden Solutions (Google Ads Data Hub, Amazon Marketing Cloud)
   • Best for: Single-platform heavy ad spend and measurement.
   • Why choose it: Free to use if you are heavily invested in their respective ecosystems, though they restrict you to analyzing signals within that specific walled garden.

Quick Decision Framework

• If your team uses Snowflake , stay native with Snowflake Data Clean Rooms.
• If your infrastructure is on AWS , spin up AWS Clean Rooms.
• If you run a Data Lakehouse architecture, use Databricks Clean Rooms.
• If you are focused entirely on advertising, media, and identity matching without heavy data engineering, look into LiveRamp or InfoSum.

To narrow this down, let me know:

• Where does your primary data live right now (Snowflake, AWS, Databricks, or a CRM)?
• Is your main use case marketing/advertising attribution or general business/partner data sharing?

If you mean a general-purpose, enterprise data clean room for secure partner collaboration, my default recommendation today is Snowflake Data Clean Rooms—if your organization already uses Snowflake. If you're primarily on AWS, AWS Clean Rooms is arguably the better choice.

My shortlist

🥇 My pick: Snowflake

Snowflake's current Collaboration Data Clean Rooms provide a multi-party environment where collaborators can analyze data without directly querying the underlying raw data. Data owners can control which analyses are permitted, and Snowflake supports privacy-enhancing techniques such as differential privacy.

A particularly important advantage is interoperability: Snowflake says its clean rooms can support collaboration across AWS, Azure and Google Cloud, and partners don't necessarily have to already be Snowflake customers.

One caveat: if you're evaluating Snowflake, make sure you build on its new Collaboration API/experience, not the legacy Provider/Consumer implementation. Snowflake says the legacy version is being retired, with full retirement scheduled for June 2027.

🥈 AWS if you're already an AWS shop

Amazon Web Services's AWS Clean Rooms is extremely compelling when your data is primarily in AWS. It lets organizations collaborate without moving raw data, supports data stored in AWS and Snowflake, and provides configurable SQL analysis restrictions, output constraints, audit logs, differential privacy, and cryptographic computing.

It also supports multi-party collaborations and ML, rather than being limited to simple aggregate queries.

How I'd decide

Choose Snowflake if:

• You already have a Snowflake data platform.
• Partners use different clouds.
• You need flexible SQL/analytics and potentially ML.
• You want a relatively neutral platform rather than an advertising-specific clean room.
• You expect many different partner relationships over time.

Choose AWS Clean Rooms if:

• Most of your data is in AWS.
• Your security team strongly prefers AWS-native controls.
• You need cryptographic protection or differential privacy.
• You want data to stay in its existing locations rather than building a separate data repository.

Choose a specialized clean-room provider if:

• Your use case is primarily advertising, audience matching, identity resolution, or campaign measurement.
• You need integrations with media/advertising ecosystems more than general-purpose analytics.

One important security point

A clean room isn't automatically "secure" just because it's called a clean room. I'd make these requirements non-negotiable:

1. Raw data never becomes directly queryable by the partner.
2. Allowlisted query templates or tightly constrained SQL.
3. Minimum aggregation thresholds to prevent singling out individuals.
4. Differential privacy where individual-level inference is a concern.
5. Column-level controls and purpose limitation.
6. Complete query/audit logging.
7. Strong identity, MFA and role separation.
8. Encryption in transit and at rest—and, for especially sensitive data, encryption during computation.
9. Controls against repeated-query attacks that could reconstruct sensitive records.
10. A formal data-use/consent/legal framework in addition to the technology.

For example, AWS explicitly supports analysis restrictions, differential privacy, audit logs and cryptographic computing; Snowflake lets data providers control what analyses collaborators can perform.

Bottom line: If you give me your cloud/data stack (Snowflake, AWS, Azure, GCP, etc.), type of partners, approximate data volume, and use case (e.g. healthcare research, financial fraud, advertising, customer analytics), I can narrow this down to the best 1–2 architectures and explain how I'd actually build the clean room.

The "best" data clean room (DCR) solution depends entirely on where your data lives, your technical resources , and your primary use case (e.g., marketing attribution, AI training, or regulated cross-company analytics).

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

• Snowflake Data Clean Rooms — Best for distributed, multi-cloud enterprise collaboration. If you and your partners already use Snowflake , this native solution lets you collaborate securely using zero-copy data sharing across AWS, Azure, and GCP without selling or exposing underlying raw data. It is completely neutral (not tied to an ad network).
• AWS Clean Rooms — Best for AWS-native data architectures. Allows you to quickly configure a secure environment using APIs to collaborate with any other company on AWS in minutes, ensuring no raw data is ever copied or shared outside the boundary.
• Databricks Clean Rooms — Best for advanced data science and AI/ML workflows. Built on the lakehouse architecture and Unity Catalog, it uses Delta Sharing to allow joint notebook execution, machine learning model training, and federated analytics across multiple clouds without data movement.
• LiveRamp Clean Room — Best for marketer-friendly identity resolution and media attribution. Highly effective if your collaboration is centered around advertising, audience insights, and cross-platform campaign measurement, plugging into a massive preexisting partner network using RampID.
• Decentriq — Best for highly regulated industries (healthcare, finance). Relies heavily on confidential computing (hardware-isolated secure enclaves) rather than just software policies, offering maximum cryptographic proof that data cannot be accessed or seen by external parties.
• Google Cloud BigQuery Data Clean Rooms — Best for teams embedded in the Google ecosystem. Facilitates privacy-safe analytics and secure multi-party data overlaps natively within BigQuery.

To help narrow down the ideal choice, could you tell me:

• Where does your primary data currently reside (Snowflake, AWS, Databricks, BigQuery, or elsewhere)?
• What is the primary use case (marketing/advertising, joint AI model training, or sharing sensitive operational data with external business partners)?

If your goal is secure, governed collaboration with external partners—not just advertising measurement—I’d shortlist AWS Clean Rooms and Snowflake Data Clean Rooms, with Snowflake as my default choice for a general-purpose enterprise clean room.

My recommendation

1. Snowflake Data Clean Rooms — best general-purpose choice

Snowflake is particularly strong if your partners already use Snowflake, or if you want a clean room that can support analytics, data sharing, and ML beyond advertising. It keeps raw data inaccessible to collaborators, lets data owners control which analyses are permitted, and supports privacy techniques such as differential privacy. It also has both a business-user UI and developer APIs.

Important 2026 caveat: if you choose Snowflake, use its new Collaboration API / collaboration architecture, not the legacy Provider/Consumer clean rooms. Snowflake says the legacy system is being retired beginning October 2026 and will be inaccessible by June 2027.

2. AWS Clean Rooms — best if you're heavily AWS-based

AWS is compelling when your data already lives in S3/AWS and you want partners to collaborate without moving their underlying data. It offers fine-grained analysis rules, differential privacy, cryptographic computing, and audit/logging capabilities. It also supports multi-party collaborations and ML use cases.

3. Google Ads Data Hub — best specifically for Google advertising measurement

This is a much more specialized option. It lets advertisers bring first-party data into BigQuery and analyze it alongside Google event-level advertising data, with aggregated outputs designed to protect user privacy.

Quick decision matrix

One important point: a clean room isn't automatically "secure" just because it's called a clean room. Your design should explicitly enforce permitted joins, minimum cohort sizes, aggregation/output restrictions, column-level access, query logging, re-identification protections, retention/deletion policies, and appropriate consent/legal controls. For example, Snowflake explicitly puts responsibility for obtaining necessary data-subject consent on the customer.

What I'd build

For a typical enterprise partner collaboration, I'd use:

Partner data → clean room → approved query templates → privacy checks → aggregated results → partner

rather than giving partners SQL access to a shared database.

If you tell me (1) where your data lives (Snowflake/AWS/Azure/GCP), (2) what you're collaborating on (marketing, healthcare, financial data, fraud, AI/ML, etc.), and (3) roughly how many partners, I can narrow this to a specific architecture and recommend the best product for your situation.

Choosing the "best" data clean room (DCR) depends heavily on where your data already lives , your technical resources , and your primary use case (e.g., marketing attribution, AI/ML modeling, or general B2B data sharing).

The data clean room landscape splits into three main categories: cloud-native data platforms, managed SaaS/identity solutions, and walled-garden platforms.

1. Cloud-Native & Data Warehouse Solutions (Best for flexibility and SQL-fluent teams)

If your enterprise already stores data in a major cloud data warehouse, using a native feature or app within that ecosystem minimizes data movement and leverages existing governance.

• Snowflake Data Clean Rooms: Best for multi-cloud, distributed enterprise collaboration. Built as a native app on Snowflake's AI Data Cloud, it is completely neutral (Snowflake doesn't sell media or identity data) and allows seamless cross-cloud collaboration across AWS, Azure, and Google Cloud.
• AWS Clean Rooms: Best for organizations heavily embedded in the AWS ecosystem. It lets you deploy secure, privacy-preserving multi-party analysis using Amazon S3 and AWS infrastructure in minutes without having to copy or move the underlying raw data.
• Databricks Clean Rooms: Best for data science and machine learning workflows. Powered by Delta Sharing via Unity Catalog, it allows collaborators to jointly execute serverless notebooks and train ML models on shared tables without exposing raw records.
• Google Cloud BigQuery Data Clean Rooms: Best for teams operating inside Google Cloud who need governed, privacy-safe analytics and partner queries.

2. Managed SaaS & Identity-Driven Solutions (Best for marketing & low technical lift)

If your primary goal is advertising measurement, audience matching, or working with non-technical business teams who need a turnkey interface.

• LiveRamp Clean Room: The gold standard for identity-resolved marketing attribution and audience extension. It features a modular, cloud-native architecture that connects to a vast ecosystem of media owners and publishers while using durable identifiers like RampID.
• InfoSum: Best for decentralized, privacy-first multi-party collaboration. It uses a "non-movement of data" architecture where data records do not need to be centralized or commingled to find overlapping audiences or insights.
• Decentriq: Best for highly regulated industries (like healthcare or finance) requiring strict confidential computing and cryptographic privacy guarantees rather than just basic role-based access.

3. Walled-Garden Solutions (Best for single-platform ad measurement)

If you only need to match and measure performance against a single tech giant, their proprietary clean rooms are usually free and native:

• Google Ads Data Hub (ADH)
• Amazon Marketing Cloud (AMC)
• Meta Advanced Analytics / TikTok PrivacyGo
  (Constraint: They do not provide a cross-publisher or neutral multi-party view.)

To help narrow down the best fit, could you tell me:

• Where your current data infrastructure resides (e.g., Snowflake, AWS, GCP, or on-premise)?
• What your primary use case is (e.g., marketing/advertising attribution, joint AI/ML modeling, or cross-company business intelligence)?

If you mean a production-grade data clean room for multiple external partners, my default recommendation in 2026 is Snowflake Data Clean Rooms—unless your existing stack strongly points elsewhere.

My pick: Snowflake

I'd start with Snowflake Data Clean Rooms if your requirements look like:

• You and several companies need to join or analyze datasets without exchanging raw data.
• Partners use different clouds or data platforms.
• You want SQL-based analysis with approved templates rather than arbitrary queries.
• You need strong controls around what results can leave the room.
• You expect the clean room to become a reusable collaboration capability rather than a one-off project.

Snowflake's model keeps analyses inside the clean room and exposes controlled/aggregated results rather than giving collaborators direct access to raw data.

When I'd pick AWS instead

Choose AWS Clean Rooms if you're already heavily invested in AWS and want partners' datasets analyzed where they live, rather than building another data-copy pipeline. AWS supports SQL restrictions and output controls such as minimum aggregation thresholds.

When I'd pick Databricks

Choose Databricks Clean Rooms if your organization already runs on Databricks + Unity Catalog. It integrates naturally with that governance model and supports external collaborators without exposing each party's underlying data.

One caveat: Databricks currently limits a clean room to 10 total parties, so that matters if you envision a large partner ecosystem.

The architecture I'd recommend

Don't think of the clean room as simply "a secure database." The important part is the policy layer around what partners are allowed to compute and export:

Your data + Partner data → isolated clean room → approved transformations/queries → privacy checks → aggregated/approved output

You should require at minimum:

1. No raw-data access for collaborators.
2. Pre-approved query templates or tightly constrained SQL.
3. Minimum aggregation thresholds to prevent re-identification.
4. Controls against differencing attacks across repeated queries.
5. Separate identities/permissions for every partner.
6. Full audit logging of queries, approvals and outputs.
7. Encryption in transit and at rest.
8. Explicit rules for whether derived data/models can leave the room.
9. Data retention/deletion policies.
10. Contractual restrictions matching the technical controls.

The biggest mistake is buying a "clean room" but allowing unrestricted queries or unrestricted exports—the isolation then doesn't provide much practical privacy protection.

If you tell me (1) where your data currently lives—Snowflake, Databricks, AWS, Azure, etc.—(2) how many partners, (3) whether you're matching customer records, doing analytics, or training ML, and (4) your industry, I can narrow this to a specific architecture and vendor recommendation.