Databricks Clear Rooms allows prospects and companions to investigate their mixed knowledge, with out revealing delicate uncooked knowledge to 1 one other. Because the GA launch on AWS and Azure in February 2025, we have seen many shoppers undertake clear rooms in Promoting, Monetary Providers, Healthcare and plenty of different industries. On this weblog publish we spotlight how main id companions are leveraging clear rooms for privateness protected identity-resolution, in addition to new collaboration and privateness functionalities that prospects have requested for.
Determine 1: Databricks Clear Rooms
Identification Decision in Clear Rooms
We’re excited to introduce an development in id decision: safe, cloud-native id decision in Databricks Clear Rooms. This innovation empowers companions and entrepreneurs to confidently and securely match and enrich datasets utilizing widespread identifiers—all with out exposing uncooked PII or transferring knowledge off-platform.
To allow this functionality, we’re thrilled to associate with business leaders together with Epsilon, Deloitte, LiveRamp, and Acxiom. Collectively, we’re making it simpler for organizations to unify fragmented data, join associated knowledge, and unlock richer insights—all inside a privacy-centric atmosphere.
Determine 2: Identification Decision in Databricks Clear Rooms
Think about an e-commerce model collaborating with a serious id supplier to resolve hashed buyer emails, postal knowledge, and gadget touchpoints, to an individual or family primarily based id. With this new functionality, each events can convey knowledge right into a clear room and carry out id decision in place—safely and effectively.
This method marks a brand new period in knowledge collaboration: one the place delicate knowledge by no means leaves the platform, id matching occurs seamlessly, and insights are generated with out compromise. Databricks is proud to energy this shift, providing a scalable atmosphere that redefines what’s potential for clean-room collaboration and id decision.
Clear Rooms on Google Cloud Platform (GCP)
Clear Rooms is now typically accessible on Google Cloud, enabling seamless cross-cloud collaborations with full flexibility. Beginning as we speak, prospects create a central clear room atmosphere on GCP and collaborate with companions throughout AWS, Azure, or some other knowledge platform. This aligns with our “Any cloud, any platform” philosophy: “Collaborate in a privacy-centric atmosphere throughout clouds, areas and knowledge platforms with out requiring knowledge motion”.
Determine 3: Create Clear Rooms in AWS, Azure and now GCP
For instance, a big retailer on GCP and a client model on AWS, seeking to associate and analyze the effectiveness of their joint advertising efforts. The retailer can now spin up a clear room on GCP and invite the model to securely contribute their engagement metrics from current marketing campaign knowledge. By combining this with the retailer’s buyer buy knowledge, each events can collaboratively establish tendencies and measure the affect of promotions—with out ever sharing or exposing their uncooked knowledge to one another.
Different clear room suppliers restrict you to collaborate on their cloud or platform. With Databricks Clear Rooms, you keep away from cloud silos and vendor lock-in and knowledge stays in place by way of Delta Sharing, and also you solely share anonymized outputs.
Multi-Occasion Collaboration
Clear Rooms now help a number of collaborators in a single room. Beforehand, every clear room was successfully two-party solely; now you’ll be able to invite as much as 9 different organizations (i.e., 10 complete). These collaborators could be on totally different clouds, areas, or knowledge platforms, but work collectively in a single central atmosphere. This unlocks “Any scale, any belief stage” and helps many-to-many collaborations with fine-grained entry controls and orchestration.
Determine 4: Multi-Occasion Collaboration Help
Think about a retail advertising situation: a retailer, its promoting model, and a market analysis agency need to construct a mixed buyer insights mannequin. All three events convey proprietary knowledge and code. With multi-party clear rooms, the retailer can invite each companions into one clear room, share essential tables, and run joint analytics. As an illustration, the retailer’s e-commerce knowledge, the model’s buyer knowledge, and the researcher’s survey knowledge could be joined and analyzed collectively, with none social gathering ever seeing one another’s uncooked tables.
By enabling multi-party collaboration with fine-grained governance, you unlock richer insights that require greater than two organizations.
Versatile Privateness Approvals
Clear Rooms now help safe self-runs, permitting collaborators to add and run their very own notebooks with specific approval from different clear room contributors. Beforehand, notebooks might solely be run by the opposite social gathering, with approval implied by clicking the run button.
Determine 5: Versatile Privateness Approvals
For instance, Hospital A desires to run a pocket book that computes joint statistics on shared affected person knowledge. They’ll now add and run that pocket book themselves — however solely after Hospital B explicitly approves the code. This balances flexibility with governance: knowledge by no means strikes with out consent, code by no means runs with out evaluate, and output at all times stays with the runner. For patrons, this implies sooner iteration, extra autonomy, and full auditability — all inside a trusted collaboration atmosphere.
Code governance is vital in knowledge collaboration. These approval options guarantee no code runs with out consent. You achieve an audit path of who permitted what (no extra shock queries), and also you cut back the danger of malicious or faulty code.
Walkthrough: Placing It All Collectively
Let’s stroll by a high-level instance utilizing all three options:
Create the Clear Room (on GCP): Knowledge Platform Lead (let’s name him Alex) units up a brand new clear room on Google Cloud. She invitations two collaborators by sharing their identifiers (AWS and Azure companions)
Add Knowledge: Every social gathering provides tables to the clear room. For instance, the AWS, GCP, and Azure datasets are all uploaded within the clear room. There at the moment are three collaborators on this room—Alex and two invitees. All three can see the shared metadata, however none can see others’ uncooked knowledge.
Add Notebooks: Alex uploads evaluation notebooks (e.g., Python scripts for ML, SQL for joins). All events should explicitly approve the pocket book earlier than Alex can run it.
Run the Evaluation: Alex now kicks off the pocket book run. As a result of it’s permitted, the serverless clean-room cluster executes the code. The run produces output tables (momentary read-only outcomes) in Unity Catalog. For instance, a joined buyer phase desk is generated so the pocket book executor can extract combination insights.
Overview and Iterate: Alex views the aggregated outputs. If new evaluation is required, they repeat, including or updating notebooks beneath the identical governance.
This demo exhibits how cross-cloud companions (on GCP, AWS, and Azure) can collaborate in a single shared clear room. It highlights multi-party collaboration, seamless knowledge sharing by way of Delta Sharing, and the power for companions to add and run their very own notebooks, with specific approvals. The consequence: safe, auditable joint evaluation throughout clouds and organizations, with no raw-data publicity.
What’s Subsequent?
Databricks Clear Rooms proceed evolving, however the core worth stays: making knowledge collaboration potential with out compromising privateness, efficiency, or platform flexibility. We invite you to discover these new capabilities and share suggestions. Able to dive deeper? Try the next periods to study extra about Clear Rooms on the Knowledge and AI Summit, June 9-12, 2025, in San Francisco
