Data clean rooms for secure cross-company collaboration

Summary

• A data clean room lets two or more organizations analyze combined data without either side seeing or copying the other's raw records; only mutually approved results leave the environment.
• The controls that make cross-company work safe are consistent: data stays in place, both parties approve the exact code that runs, outputs are limited to aggregates or governed tables, and every action is logged.
• Cross-company analysis often spans different clouds, so a clean room should let partners collaborate without forcing anyone to migrate data.
• Databricks Clean Rooms is generally available and runs across AWS, Azure, and Google Cloud, built on Delta Sharing and serverless compute so collaborators work on shared data in an isolated environment without moving it.
• Governance comes from Unity Catalog: clean rooms are securable objects with fine-grained privileges, audit logging, and egress controls, and they support HIPAA-regulated data.

Data clean rooms for secure cross-company collaboration

A data clean room is the standard way for separate companies to combine and analyze sensitive data without exposing raw records to each other. It keeps each party's data in place, runs only code both sides have approved, and returns agreed outputs such as aggregates or governed tables. That structure lets a brand, a partner, and a measurement provider answer a shared question while personal data stays private and neither side can read the other's rows. Databricks Clean Rooms delivers this on Delta Sharing and serverless compute, generally available and running across AWS, Azure, and Google Cloud.

What is a data clean room?

A data clean room is a governed environment where multiple organizations analyze combined data sets without revealing the underlying data to one another. Each party contributes data, both sides agree on the code that runs, and the environment returns only approved outputs. Collaborators can see table and column names and types so they can write meaningful queries, but not the rows themselves. Because the work happens inside a neutral, isolated space, a clean room answers questions that need more than one company's data, such as audience overlap, attribution, or joint research, without either party handing over a copy of its data.

Key capabilities to evaluate

Capability	Why it matters
No data movement	Partners analyze shared data in place; raw records are never copied or made visible.
Mutually approved code	Both sides review and approve the code before it runs, so neither can quietly query the other's raw data.
Output and egress controls	Results are limited to aggregates or governed tables, and outbound network access can be restricted.
Cross-cloud reach	Partners on different clouds can collaborate without a migration.
Fine-grained governance and audit	Access control plus a logged record of every action make results defensible.
Multi-party support	More than two organizations can join one collaboration when a question needs several data sets.
Isolation with no backdoor	The shared environment is neutral, and no party holds admin access to it.

How Databricks approaches clean rooms

Databricks Clean Rooms runs analysis on serverless compute in an isolated, Databricks-managed environment in a region the creator selects, so collaborators work with shared tables, volumes, and models without copying each other's data.

• Raw data stays behind the boundary. Only table and column metadata is visible to other parties. Row-level data is never exposed, and results are constrained to aggregations or governed output tables written back to the runner's own catalog.
• Approved code only. Either collaborator can add SQL or Python notebooks, all parties review and approve them before execution, and only the most recent approved version can run.
• Isolated, ephemeral compute. The central clean room runs on temporary serverless hosts with no data persistence after a job completes, and no party has admin privileges over it.
• Cross-cloud by design. Because it rides on Delta Sharing, a partner on one cloud and a partner on another can collaborate in one clean room without moving data.
• Governed by Unity Catalog. A clean room is a securable object in Unity Catalog with fine-grained privileges for who can create, modify, and run tasks. Actions are recorded in system tables and account audit logs, and serverless egress control can block outbound connections to unauthorized destinations.
• Regulated data support. Clean Rooms support HIPAA-regulated data, so healthcare and other sensitive collaborations can run under the same controls.

For a plain-language walk through how this differs from simple data sharing, see the Clean Rooms Q&A and what's new in Databricks Clean Rooms.

Use cases

• Marketing measurement and attribution. A brand joins its outcome data with a partner's exposure data to measure reach, attribution, and campaign lift, without either side revealing raw records.
• Audience overlap and identity resolution. Two parties measure how much their audiences overlap to plan media and cut wasted spend, without exchanging identifiers.
• Cross-industry data collaboration. Supply-chain partners, financial institutions, or research teams combine data sets for shared analysis while each keeps ownership of its own data.
• Regulated-data collaboration. Because Clean Rooms support HIPAA-regulated data, healthcare and life-sciences partners can collaborate on sensitive data sets under governed controls.

FAQs

How is a data clean room different from data sharing?

Data sharing gives one party governed read access to another's data. A clean room adds a private, neutral space for multi-party analysis where partners join data, run mutually approved code, and get back only agreed-upon results, without anyone reading the other's rows.

Does a clean room require copying data to a shared location?

No. On Databricks, Clean Rooms use Delta Sharing, so analysis runs against shared data in place. Raw records are not copied, and only approved outputs leave the environment.

Can partners on different clouds collaborate?

Yes. Databricks Clean Rooms support collaboration across AWS, Azure, and Google Cloud, so partners on different clouds can work together without migrating data.

Is data in a clean room protected for regulated workloads?

Data stays in each party's control, results are limited to approved outputs, and every action is logged through Unity Catalog. Databricks Clean Rooms also support HIPAA-regulated data. Confirm the specific controls that apply to your workload with Databricks.