Top 10 Questions You Asked About Databricks Clean Rooms, Answered

Data collaboration is the backbone of modern AI innovation, especially as organizations collaborate with external partners to unlock new insights. However, data privacy and intellectual property protection remain major challenges in enabling collaboration while safeguarding sensitive data.

To bridge this gap, customers across industries are using Databricks Clean Rooms to run shared analysis on sensitive data and enable privacy-first collaboration.

We have compiled below the 10 most frequently asked questions about Clean Rooms. These cover what Clean Rooms are, how they protect data and IP, how they work across clouds and platforms, and what it takes to get started. Let's jump in.

1. What is a “data clean room”?

A data clean room is a secure environment where you and your partners can work on sensitive data together to extract useful insights, without sharing the underlying sensitive raw data.

In Databricks, you create a clean room, add the assets you want to use, and run only approved notebooks within an isolated, secure and governed environment.

2. What are some example use cases of clean rooms?

Clean rooms are useful when multiple parties need to analyze sensitive data without sharing their raw data. This is often due to privacy regulations, contracts, or the protection of intellectual property.

They are used across many industries, including advertising, healthcare, finance, government, transportation, and data monetization.

Some examples include:

Advertising and marketing: Identity resolution without exposing PII, campaign planning and measuring, data monetization for retail media, and brand collaboration.

• Partners such as Epsilon, The Trade Desk, Acxiom, LiveRamp, and Deloitte utilize Databricks clean rooms for identity resolution.

Financial Services: Banks, insurers, and credit card companies combine data for better operations, fraud detection, and analysis.

• Examples: Mastercard uses clean rooms to match and analyze PII data for fraud detection; Intuit securely matches borrower data with lenders to find qualified borrowers.

Clean rooms protect customer data while allowing collaboration and data enrichment.

3. What kinds of data assets can I share in a clean room?

You can share a wide-range of Unity Catalog-managed assets in Databricks Clean Rooms:

• Tables (Managed, External, and Foreign): structured data like transactions, events, or customer profiles.
• Views: filtered or aggregated slices of your tables.
• Volumes: files such as images, audio, documents, or private code libraries.
• Notebooks: SQL or Python notebooks that define the analysis you want to run.

Here’s how it looks like in practice:

• A retailer, a CPG brand, and a market research firm share anonymized views including: hashed customer IDs, aggregated sales metrics, and regional demographics to jointly analyze campaign reach.
• A streaming platform and an advertising agency share campaign impression tables and a notebook that computes cross-platform audience metrics.
• A bank and a fintech partner share volumes containing risk and fraud ML models and use a notebook to jointly score the models while keeping individual records private.

4. How does this compare to Delta Sharing? Why would I use a clean room instead?

Think of it this way: Delta Sharing is the right choice when one party needs read-only access to data in their own environment and it is acceptable for them to see the underlying records.

Clean Rooms add a secure, controlled space for multi-party analysis when data must stay private. Partners can join data assets, run mutually approved code, and return only the outputs that all sides agree on. This is useful when you must meet strict privacy guarantees or support regulated workflows. In fact, data shared in Clean Rooms still uses the Delta Sharing protocol behind the scenes.

For example, a retailer might use Delta Sharing to give a supplier read‑only access to a sales table so they can see how products are selling. The same pair would use a Clean Room when they need to join richer, more sensitive data from both sides (like customer traits or detailed inventory), run approved notebooks, and only share aggregated outputs such as demand forecasts or top at‑risk items.

5. How is sensitive data and IP protected in the clean room?

Clean Rooms are built so your partners never see your raw data or IP. Your data remains in your own Unity Catalog, and you only share specific assets in the clean room through Delta Sharing, which is controlled by approved notebooks.

To enforce these protections in a clean room:

• Collaborators only see schemas (column names and types), not the actual row-level data.
• Only notebooks that you and your partners approve can run on serverless compute in an isolated environment.
• Notebooks write to temporary output tables, so you control exactly what leaves the clean room.
• Outbound network traffic is restricted through serverless egress controls (SEG).
• To protect IP or proprietary code, you can package your logic as a private library, store it in a Unity Catalog volume, and reference it within clean room notebooks without revealing your source code.

6. Can collaborators on different clouds join the same clean room?

Yes. Clean Rooms are designed for multicloud and cross-region collaboration as long as each participant has a Unity Catalog–enabled workspace and Delta Sharing enabled on their metastore. This means an organization using Databricks on Azure can collaborate in a clean room with partners on AWS or GCP.

7. Can I bring data from Snowflake, BigQuery, or other platforms into a clean room?

Yes, absolutely. Lakehouse Federation exposes external systems like Snowflake, BigQuery, and traditional warehouses as foreign catalogs in Unity Catalog (UC). Once the external tables are available in UC, you share them in the clean room the same way you share any other table or view.

Here’s how it works at a high level: you use Lakehouse Federation to create connections and foreign catalogs that expose external data sources in Unity Catalog, without having to copy all that data into Databricks. Once those external tables are available in Unity Catalog, you can share them into a Clean Room just like any other Unity Catalog–managed table or view.

8. How do I run a custom analysis on joint data?

Inside a clean room, you do almost everything through notebooks. You add a SQL or Python notebook that includes the code for the analysis you want, your partners review and approve the notebook, and then it can run.

Simple case: you might have a SQL notebook that counts overlapping hashed IDs between a retailer’s purchases and a media partner’s impressions, and then spits out reach, frequency, and conversion.

More advanced: you use a Python notebook to join features from both sides, train or score a model on the combined data, and write predictions to an output table. The approved runner sees the outputs, but no one sees the other side’s raw records.

9. How does multi-party collaboration work?

In a Databricks Clean Room, you can have up to 10 organizations (you plus 9 partners) working together in one secure environment, even if you’re on different clouds or data platforms. Each team keeps its data in its own Unity Catalog and only shares the specific tables, views, or files they want to use in the clean room.

Once everyone is in, each party can propose SQL or Python notebooks, and those notebooks need approval before they run, so all sides are comfortable with the logic.

10. So, all that sounds good. How do I get started?

Here’s a simple way to get started:

• Check that your workspace has Unity Catalog, Delta Sharing, and serverless compute enabled.
• Create a Clean Room object in your Unity Catalog metastore and invite your partners with their sharing identifiers.
• Each party adds the data assets and notebooks they want to collaborate on.
• Once everyone approves the notebooks, run your analysis and review the outputs in your own metastore.

Watch this video to learn more about Clean Room creation and getting started.