From AWS to Snowflake, Data Clean Rooms are trapped between privacy and progress

Data clean rooms were supposed to bridge the gap between privacy and collaboration. But many enterprise users are discovering that this promise remains only partially fulfilled — particularly across leading platforms like AWS, Snowflake, and Databricks.

For advertisers, publishers, retailers, and data partners eager to unlock insights from sensitive customer data while remaining compliant with privacy regulations, the current state of clean rooms presents a dilemma: either limit what’s shared, or accept privacy risks.

So why are clean rooms still falling short — and how can we fix it?

Why data clean rooms matter more than ever

As consumer expectations and regulatory demands evolve, secure data collaboration has become essential. Clean rooms offer a controlled environment where multiple parties can collaborate on and analyze first-party data or other sensitive information. Depending on the data clean room, this can be done without directly exposing raw or underlying data.

Whether optimizing advertising campaigns, improving healthcare patient experiences, or generating unique business insights, data clean rooms are meant to enable privacy-enhanced collaboration in a secure environment.

But as we hinted above, not all clean rooms are created equal.

The current landscape’s big players: AWS, Snowflake, and Databricks

Let’s take a look at how today’s most well-known data clean rooms compare and where they hit roadblocks.

AWS Clean Rooms

Strengths: Deep integration with the AWS ecosystem, basic query controls, can be used cross-cloud or cross-platform.

Limitations: The burden is often on users to enforce data handling policies and ensure compliance. No-code functionalities are available only for simple queries.

Best for: Technical teams already embedded in AWS and/or Snowflake who are looking for flexible but self-managed collaboration, as well as those with some coding experience.

Snowflake data clean room

Strengths: Popular among marketers for ease-of-use; built-in features like Secure Data Sharing and object tagging for access controls.

Limitations: Snowflake’s clean room solution was primarily built for organizations already using Snowflake’s platform. As a result, it lacks native cross-cloud support, limiting collaboration across different data stacks or cloud environments. This can make it difficult for brands and media partners operating in heterogeneous infrastructures to fully collaborate without significant data movement or platform alignment.

Best for: Organizations already invested in Snowflake’s cloud platform with modest privacy requirements. 

Learn more in our deep dive: Decentriq vs Snowflake data clean rooms

Databricks clean rooms

Strengths: Enable secure, privacy-preserving data collaboration without moving or exposing raw data. These clean rooms offer fine-grained access control and support advanced analytics across clouds and platforms.

Limitations: Databricks clean rooms are built primarily for technical users, with a focus on notebook-based workflows and Spark environments. There is no dedicated user interface for marketers or business teams, making the platform challenging to use without engineering support. This limits accessibility and slows time-to-value for non-technical collaborators who need to explore or activate data without relying on code.

Best for: Best suited for cross-company data collaboration in regulated industries like healthcare and finance. Ideal for use cases such as joint marketing analytics, secure audience matching, and shared risk or supply chain analysis—where data privacy and governance are critical.‍

Why user experience is the real differentiator

While features like access control, encryption, and analytics support are essential, the usability of a data clean room can make or break your success with it — especially in fast-moving marketing and media environments.

Platforms like Databricks are powerful but require technical expertise to operate, often depending on notebooks, Spark, and Python workflows. Snowflake, while more accessible, still assumes users are comfortable navigating its SQL-based environment and managing roles and permissions across datasets.

In contrast, Decentriq is designed for business users first. Its no-code interface makes it easy to define analysis rules, manage collaborators, and activate insights all within a secure, governed environment. This means teams can focus on outcomes, not infrastructure, and start collaborating immediately without waiting on engineering resources.

In the end, a clean room is only as useful as it is usable — and that’s where Decentriq stands apart.

The problem: a tradeoff between privacy and progress

In each case, the core issue is the same: users must trade privacy for utility.

• Want full analytical flexibility? Then data movement and leakage risks increase
  
  
• Want locked-down privacy? Then you're limited to preset queries or heavily sanitized datasets
  
  

That tradeoff is holding back real innovation — especially in sectors like media, advertising, and retail, where data clean room collaborators need to extract actionable, granular insights while upholding strict data privacy standards.

The solution: clean rooms that don’t compromise

Decentriq is built to resolve this tension — delivering both utility and privacy in a truly secure data clean room environment.

Powered by confidential computing, Decentriq clean rooms enable parties to collaborate on sensitive data without ever revealing the underlying dataset — not even to Decentriq itself.

• Verifiable privacy: End-to-end encryption and audit trails that prove compliance.
  
  
• Rich analytics: Support for advanced queries, lookalike modeling, and campaign analysis.
  
  
• No-code UX: Built for marketers and data scientists alike. Easily define analysis rules and control query access.
  
  
• Flexible integrations: New audience connectors for seamless onboarding from ad platforms and CRMs.
  

Real-world results from brands like Samsung and IKEA

Global leaders are already proving the impact of secure, collaborative analytics with Decentriq.

• Samsung + Publicis used Decentriq to analyze cross-publisher campaign performance in a cookieless, privacy-safe way. Read the case study
  
  
• IKEA + willhaben launched a first-party-driven campaign and achieved significant performance improvements using Decentriq’s clean rooms. Read the case study
  

The takeaway: clean rooms need a reset

It’s time to move beyond the false choice between data utility and privacy. Solutions like AWS, Snowflake, and Databricks offer partial progress — but only Decentriq delivers privacy-enhanced data collaboration without compromise.

If your organization is working with consumer data, sensitive campaign results, or first-party insights — and you want confidence in both compliance and capability — it’s time to see the difference.

Ready to experience the difference?

See how Decentriq empowers media partners, data owners, and marketers to do more with clean rooms — without sacrificing privacy.

Book your demo today