A retailer and a media publisher both wanted the same number — how many of the retailer's customers had also seen the publisher's ad campaign — and neither one was willing to hand over their customer list to get it. That's not a hypothetical; it's the single most common reason a data clean room gets bought, and it's a narrower, more specific tool than the "share data safely" pitch makes it sound. A clean room doesn't make data sharing safe in general — it makes one specific class of question answerable (aggregate, rule-constrained joins across two parties' data) without either party ever seeing the other's raw rows. That's a real and valuable capability, and it's also exactly as governed as the rules you configure it with, not automatically safe by virtue of the label.
This is AWS Clean Rooms and Snowflake Data Clean Rooms compared on the mechanics that actually matter: how each enforces "no raw data leaves," what privacy-enhancing technology each adds on top of basic access control, deployment and ecosystem differences, real use cases beyond the advertising pitch, and the lesson that bit a project I advised on.
What does a data clean room actually solve, precisely?
A data clean room is a governed environment where two or more parties can run agreed-upon queries or models against each other's data without either party gaining direct access to the other's underlying rows — the platform enforces what questions can be asked and what granularity of answer comes back, rather than trusting either party to self-police what they extract. The classic shape is audience overlap: "how many of my customers match your customers, and what does the overlapping segment look like in aggregate" — answerable without either side ever seeing the other's actual customer list, because the platform computes the join and returns only the aggregate result the agreed rule permits.
The precision matters because clean rooms get pitched as a general privacy solution, and they're not — they're a specific technical answer to collaborative analysis between parties who don't trust each other with raw access, which is a different problem than protecting data within a single organization (that's tokenization, masking, and differential privacy applied internally) or governing who inside one company can see what (that's row/column-level access control). Clean rooms sit specifically at the boundary between two separate parties' data.
How does AWS Clean Rooms enforce that boundary?
AWS Clean Rooms lets each party bring data (from S3, Redshift, or other AWS sources) into a collaboration without copying it into a shared location — queries run against each party's data where it lives, under analysis rules that constrain what can be extracted. The platform supports three collaboration modes with genuinely different power and risk profiles: SQL analysis rules (constrained, aggregate-only SQL — the most restrictive and most common starting point), custom analysis rules (bring your own PySpark job, useful when the collaboration needs logic SQL can't express cleanly), and bringing your own ML model to run against a partner's data without either party seeing the other's model or raw rows. Layered on top: AWS Clean Rooms Differential Privacy adds calibrated statistical noise to query results specifically to prevent re-identification of individuals even from aggregate output, with the privacy controls exposed as managed settings rather than requiring differential-privacy expertise to configure; cryptographic computing options add encryption-in-use for the most sensitive collaborations; and analysis logs give every party an audit trail of what queries actually ran, which matters for exactly the trust question a clean room exists to answer — "prove to me nothing beyond the agreed rule happened."
How does Snowflake Data Clean Rooms take a different approach to the same problem?
Snowflake Data Clean Rooms is delivered as a Native App — the collaboration logic runs inside each party's own Snowflake account, under that party's own existing governance, rather than data being brought into a separate, third collaboration environment. This is the structural difference worth understanding: AWS Clean Rooms is a distinct service parties bring data into; Snowflake's approach keeps each party's data inside the Snowflake perimeter it's already governed by, and ships the clean room's rule-enforcement logic to the data instead of the data to a shared service. For a Snowflake-native shop, this means clean room collaboration inherits whatever masking, row access policies, and audit controls (Horizon governance) already apply to that data — no separate governance surface to reconcile against a second platform's model.
Snowflake's own differentiators: a no-code collaboration UI aimed at business users configuring a partnership without needing SQL or a data-engineering ticket, Snowpark Container Services-backed compute for running heavier ML workloads inside the clean room without exporting anything, and its own differential-privacy and cryptographic-compute options for the collaborations that need them. Availability is real but not universal — Data Clean Rooms is generally available specifically on AWS-hosted and Azure-hosted Snowflake accounts in a defined set of regions, which is a genuine constraint to check before assuming it's available wherever your Snowflake account happens to run.
graph TD
subgraph A["Party A's environment"]
DA["Party A data
(stays in place)"]
end
subgraph B["Party B's environment"]
DB["Party B data
(stays in place)"]
end
RULES["Agreed analysis rules
(SQL / custom / ML)"]
DA --> RULES
DB --> RULES
RULES -->|"only the agreed
aggregate/rule-constrained output"| RESULT["Result each party
is allowed to see"]
DA -.->|"raw rows never exposed"| B
DB -.->|"raw rows never exposed"| A
The core clean room guarantee, regardless of platform: each party's raw data never crosses to the other party. Only the output an agreed rule explicitly permits — an aggregate count, a model's prediction, a differentially-private statistic — leaves the boundary. The platforms differ in where the rule-enforcement compute actually runs (a shared AWS service vs. inside each party's own Snowflake account), not in that core guarantee.
| AWS Clean Rooms | Snowflake Data Clean Rooms | |
|---|---|---|
| Deployment model | Separate collaboration service; data referenced from S3/Redshift | Native App running inside each party's own Snowflake account |
| Analysis modes | SQL rules, custom PySpark rules, bring-your-own ML model | No-code UI, SQL, Snowpark Container Services for heavier ML |
| Privacy tech | Differential privacy, cryptographic computing, analysis logs | Differential privacy, cryptographic compute options, inherited Horizon governance |
| Best fit | Multi-cloud or non-Snowflake data sources, AWS-native ecosystems | Both parties already on Snowflake, want to inherit existing governance |
What are the real use cases beyond advertising measurement?
Advertising and media measurement is the use case both vendors lead with, and for good reason — it's the cleanest illustration of the pattern (a brand and a publisher both want campaign-effectiveness numbers, neither wants to hand over its customer graph) — but it's not the ceiling. In healthcare and life sciences, a pharmaceutical company and a health system can collaborate on post-market safety signals or patient-cohort identification for a trial without the health system exposing individual patient records, a direct extension of the same evidence-layer discipline regulated healthcare AI already needs — see the RWE clinicogenomics governance piece for Snowflake Data Clean Rooms deployed inside a real HIPAA/RWE compliance stack, alongside tokenization, masking, and data contracts. In financial services, banks can collaborate on fraud-pattern or money-laundering detection across institutions — a case where the entire point is that no single bank should see another bank's account-level data, but a fraud ring operating across both absolutely should be detectable in the aggregate signal. In insurance, a carrier can enrich underwriting with market-level driving-population insights from a partner without either side exposing individual policyholder data.
What's the lesson learned that actually matters here?
A clean room is an access surface with rules, not a governance program by itself — and the mistake I've seen teams make is treating "we bought a clean room" as equivalent to "we solved the data-sharing risk," when the actual risk moved, it didn't disappear. The analysis rule you configure is the control; a badly designed SQL analysis rule (aggregation thresholds set too low, allowing a query crafted to isolate a near-singleton group) or an overly permissive custom rule can leak more than either party intended, and the platform enforces exactly the rule you wrote, not the rule you meant to write. This is precisely the same discipline as designing a k-anonymity or differential-privacy threshold for any other re-identification-sensitive release — the clean room doesn't remove that design responsibility, it just relocates where the design decision lives.
I've watched a clean room collaboration get approved by both legal teams on the strength of "it's a clean room, raw data never leaves" — and the actual analysis rule that shipped allowed a minimum aggregation threshold low enough that a motivated analyst could isolate a group of two or three individuals by crafting sequential queries. Neither platform prevents this by default; it's a configuration choice, and "we're using a clean room" is not itself a privacy guarantee independent of how tightly that configuration is set. Before signing off on a clean room collaboration, review the actual minimum-aggregation and differential-privacy-budget settings as a security control in their own right — with the same scrutiny you'd give a masking policy or an access grant — not as a formality the platform's name already covers.
What to carry away
Both platforms deliver the same core guarantee — raw data never crosses the boundary between collaborating parties, only agreed, rule-constrained output does — through structurally different deployment models: AWS Clean Rooms as a distinct collaboration service data is referenced into, Snowflake Data Clean Rooms as a Native App running inside each party's own Snowflake account and inheriting its existing governance. Neither is universally better; AWS fits multi-cloud or AWS-native data estates, Snowflake fits parties who are both already Snowflake-native and want to avoid reconciling a second governance model.
The use cases go well past advertising measurement — healthcare cohort collaboration, cross-institution fraud detection, and insurance underwriting all fit the same shape — but the discipline that actually determines whether a clean room is safe is the analysis rule and aggregation-threshold configuration, not the product label. Review those settings with the same rigor as any other access control, because a clean room enforces exactly the rule you configured, and a loose rule leaks exactly as much as a loose masking policy would.