Data Clean Rooms

1. Data Clean Rooms

Secure data sharing gives a consumer direct read access to a provider's data. But sometimes sharing raw data — even read-only — is too much. Two companies might want to collaborate on their data without either party seeing the other's raw records at all. That's the problem Data Clean Rooms solve.

2. Collaboration Without Exposure

Claro wants to run a joint marketing campaign with RetailCo. The question is simple: how many customers appear in both datasets? That answer would tell them whether a joint offer is worth pursuing. But Claro can't share its customer list with RetailCo, and RetailCo can't share theirs with Claro — both lists contain sensitive personal and financial data. Standard data sharing doesn't help: it would give each party visibility into the other's raw records. A clean room is designed exactly for this.

3. What is a Data Clean Room?

A Snowflake Data Clean Room is a secure environment - built on the Native App Framework - where two parties can collaborate on their data without either party seeing the other's raw records. Each party contributes their data. Inside the clean room, approved queries run against the combined data. Only the result - an aggregate or a count - comes back out. The raw records never leave the clean room.

4. How Do Snowflake Clean Rooms Work?

Both parties contribute their data. Claro's customer records and RetailCo's customer records enter the secure environment. An approved query runs inside — in this case, a join on a hashed customer identifier to count the overlap. The result is a single number. The raw records never leave the clean room. Neither party can inspect what the other contributed. The clean room enforces this boundary through the query logic built into the Native App.

5. Provider and Collaborator Roles

Clean rooms have two roles. The provider — Claro — creates and configures the clean room: defining which queries are permitted, what data can be joined, and what results can be returned. The collaborator — RetailCo — joins the clean room, contributes their data, and runs the approved queries. The collaborator cannot modify the query logic or change access rules. That asymmetry is by design: the provider maintains control over the privacy boundary throughout. This is what makes clean rooms suitable for regulated industries where one party needs to remain in full control of how collaboration proceeds.

6. Clean Rooms vs Secure Data Sharing

The distinction comes down to one question: does the other party need to see your raw data, or do they just need the answer? Secure data sharing gives the consumer direct access to query, filter, and explore. A clean room gives neither party access to the other's raw data, just the result of a jointly approved query. Common clean room use cases include audience overlap analysis, joint campaign measurement, and fraud detection consortia where multiple institutions share signals without exposing their underlying records.

7. Let's practice!

Now test your understanding of clean rooms versus secure sharing. Let's practice.

Create Your Free Account

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.