The architect’s playbook for Snowflake Data Clean Rooms

A hands-on journey through secure data collaboration

Welcome to the modern era of data architecture, where sharing your company’s most valuable asset — its data — feels a little like sharing your toothbrush. Generally frowned upon. Inherently risky. And closely watched by authorities who are very, very happy to hand out eye-watering fines when you get it wrong.

For decades, “data collaboration” meant something genuinely grim: you exported a giant flat file, encrypted it, flung it across an SFTP server, and then quietly prayed that whoever caught it on the other end had remembered to lock their doors. That was the whole protocol.

But the ground has shifted. Third-party cookies are crumbling. Privacy frameworks like GDPR and CCPA now police how personally identifiable information (PII) gets handled, and they police it with teeth. So organizations are stuck inside an awkward paradox: they desperately need to extract mutual value from shared datasets to compete in a data-driven economy — but they absolutely cannot, legally or ethically, hand over the raw data to do it.

Enter the Snowflake Data Clean Room.

After Snowflake acquired the clean room technology provider Samooha in late 2023, the platform folded distributed clean room capabilities right into its core. Built on the Snowflake Native App Framework, Data Clean Rooms let organizations collaborate without their data ever wandering outside their governance perimeter. The data stays home. The insights still travel. Everybody wins, and nobody emails a CSV.

This guide walks you through the whole thing end to end — architecture, deployment, and the operational reality of running a clean room. And rather than march you through abstract theory, we’re going to follow a story. You’ll play lead engineer at two (entirely fictional) companies, writing the code, clicking through the UI, and building the privacy policies that make secure collaboration actually work.

Setting the stage: A tale of two data giants

Before the “how,” we need the “why.” Our walkthrough centers on a high-stakes partnership between two companies that exist only in this article.

GalacticMedia is a colossal digital publisher. Streaming services, digital magazines, news portals — they own a sprawling media empire. Which means they’re sitting on a massive, deeply sensitive first-party dataset: millions of subscriber profiles, complete with hashed email addresses, content preferences, and demographic segments. Their plan is to turn that data into money by selling sharply targeted premium ad inventory.

CosmicGarb is a trendy direct-to-consumer retail brand. They’ve got a rich customer database — purchasing behavior, lifetime value (LTV), full transaction history. They want to advertise on GalacticMedia’s platform, but they’re not interested in spraying budget at strangers. They only want to reach people who look a lot like their existing high-value VIP customers.

The mission is straightforward: CosmicGarb wants an Audience Overlap & Segmentation analysis. Where does their VIP customer list intersect with GalacticMedia’s subscriber base?

In the bad old days, CosmicGarb would have emailed a CSV of their VIP customers straight to GalacticMedia — a security disaster wearing a friendly attachment icon. With a Snowflake Data Clean Room, GalacticMedia (the Data Provider) and CosmicGarb (the Data Consumer) can query the overlap, pull out aggregate insights, and never once lay eyes on each other’s raw PII.

The sandbox: prerequisites and licensing

Before our engineers touch a line of SQL, they need to know the house rules. What a clean room can actually do depends heavily on which Snowflake edition each party is running.

Here’s how the capabilities break down:

The takeaway: GalacticMedia, as the Provider, must run Enterprise Edition or higher to use the policy enforcement that makes sharing safe in the first place. CosmicGarb, as a pure Consumer, could technically scrape by on Standard Edition just to peek at overlap metrics — but since they want to Activate their enriched audience (push results out to an ad platform), they need Enterprise too. One more thing: trial accounts can’t host or join clean rooms at all.

The three pillars of privacy: Engineering the Shield

Here’s the thing people miss about clean rooms. The magic isn’t joining the tables. Joining tables is easy, we’ve all done it. The magic is the programmatic, mathematically airtight guarantee that nobody can reverse-engineer or quietly siphon off the raw data.

So before GalacticMedia invites CosmicGarb in, GalacticMedia’s lead engineer needs to build a fortress around their SUBSCRIBERS table.

Snowflake provides the bricks for that fortress: native data protection policies. These are schema-level security objects that lurk inside the system, intercept incoming queries, and evaluate them on the fly. There are three of them, and we’re going to code all three.

Pillar 1: Aggregation Policies — the power of the crowd

Aggregation Policies are your first line of defense against someone fishing for granular, row-level data. They enforce two rules on every consumer query:

1. The table must be queried with an aggregation function — SUM(), COUNT(), AVG(), that family.
2. Each resulting group must contain at least a minimum number of records, set by the MIN_GROUP_SIZE parameter.

Picture the danger. Suppose CosmicGarb runs a query for the content preferences of male subscribers in one specific zip code, who hold a premium subscription, and who signed up on a particular Tuesday. If exactly one human being matches that description… congratulations, you’ve just identified a single real person. That’s the “small cell” problem, and an Aggregation Policy exists to make it impossible.

Coding a row-based Aggregation Policy. GalacticMedia’s engineer decides no query may return anything representing fewer than five distinct rows. The policy gives administrators a free pass but holds analysts to the rule:

CREATE OR REPLACE AGGREGATION POLICY galactic_agg_policy
AS () RETURNS AGGREGATION_CONSTRAINT ->
CASE
    WHEN CURRENT_ROLE() = 'DCR_ADMIN' THEN NO_AGGREGATION_CONSTRAINT()
    ELSE AGGREGATION_CONSTRAINT(MIN_GROUP_SIZE => 5)
END;

Then they bolt it onto the table:

ALTER TABLE subscribers
SET AGGREGATION POLICY galactic_agg_policy;

Now, if a CosmicGarb analyst tries a hopeful SELECT * FROM subscribers;, Snowflake's compiler shuts the door instantly — no aggregate function, no result.

But here’s the genuinely clever part. If the analyst runs a compliant query — say, SELECT state, COUNT(*) FROM subscribers GROUP BY state; — Snowflake runs it happily. And when a particular group falls short of MIN_GROUP_SIZE, Snowflake doesn't throw a tantrum and fail the whole query. If only three subscribers live in Wyoming, those three get quietly swept into a "remainder group," and the GROUP BY key (the state name) comes back as NULL. The identifying detail vanishes; the macro-level math stays accurate. Wyoming's data still counts — Wyoming just doesn't get named.

The “whale” problem. Counting rows is a fine start, but it has a hole in it. Imagine a transaction table. One hyper-active customer — a “whale” — racks up six separate purchase rows. A query grouping on that whale’s behavior happily clears the MIN_GROUP_SIZE => 5 bar... while exposing exactly one person. Five rows, one human.

Snowflake’s fix is the ENTITY KEY. Instead of counting raw rows, the policy counts distinct entities — distinct humans, distinct households, whatever you define. GalacticMedia updates the policy to key on the subscriber's hashed email:

ALTER TABLE subscriber_transactions
SET AGGREGATION POLICY galactic_agg_policy
ENTITY KEY (hashed_email);

Now the aggregation engine understands that every row sharing a hashed email belongs to one entity. Every output row is guaranteed to represent at least five distinct human beings — no matter how many thousands of rows those people generated between them. And it works even if hashed_email never appears in the query itself. The policy is watching regardless.

Pillar 2: Projection Policies — the visibility cloak

A quick refresher from relational algebra: a query has selections (filtering rows with WHERE or JOIN) and projections (the columns you list in SELECT).

A Projection Policy does something elegant — it severs the link between the two. A column can be used heavily inside a query’s plumbing (joining tables, for instance) while being strictly forbidden from showing up in the final output.

This is exactly what our story needs. CosmicGarb has to use hashed_email to run the Audience Overlap join. But GalacticMedia absolutely cannot let CosmicGarb SELECT hashed_email and dump the resulting list of real people into their own database.

Coding the Projection Policy:

CREATE OR REPLACE PROJECTION POLICY cloak_email_policy
AS () RETURNS PROJECTION_CONSTRAINT ->
CASE
    WHEN CURRENT_ROLE() = 'DCR_ADMIN' THEN PROJECTION_CONSTRAINT(ALLOW => true)
    ELSE PROJECTION_CONSTRAINT(ALLOW => false, ENFORCEMENT => 'NULLIFY')
END;

Unauthorized roles can’t project the column — but notice ENFORCEMENT => 'NULLIFY'. Instead of crashing the query when the forbidden column gets requested, Snowflake just serves that column back as NULL. This is a real gift for automated data science pipelines, which tend to expect a particular schema shape and get grumpy when it changes. (Prefer a hard stop? Swap in ENFORCEMENT => 'FAIL' and the query dies on the spot.)

Then attach it to the specific column:

ALTER TABLE subscribers
MODIFY COLUMN hashed_email
SET PROJECTION POLICY cloak_email_policy;

Now CosmicGarb can join their VIP_CUSTOMERS table to GalacticMedia's SUBSCRIBERS table ON vip.hashed_email = sub.hashed_email, and the engine computes the intersection flawlessly. But the moment an analyst writes SELECT sub.hashed_email, the system intercepts the request and hands back a polite, empty void.

And before anyone gets sneaky: Snowflake tracks column lineage. Try to dodge the policy by building a fresh view on top of the protected table, and Snowflake traces the lineage back to the base table and blocks the projection in your shiny new view too. The cloak follows the column wherever it goes.

Pillar 3: Join Policies — forcing the handshake

The last pillar is the Join Policy. In a collaboration, the provider’s data is only valuable — and only safe — when it’s crossed with the consumer’s data. A provider really doesn’t want a consumer treating the clean room like a free open API, querying the provider’s dataset all on its own.

A Join Policy enforces relational dependency. Any query against the protected table must include a JOIN against another distinct table or view. No join, no service.

CREATE OR REPLACE JOIN POLICY require_collaboration
AS () RETURNS JOIN_CONSTRAINT ->
JOIN_CONSTRAINT(JOIN_REQUIRED => TRUE);

ALTER TABLE subscribers
SET JOIN POLICY require_collaboration;

If a curious analyst at CosmicGarb tries to query SUBSCRIBERS alone — just to sketch out GalacticMedia's overall demographics — Snowflake halts execution and returns SQL compilation error 506037 (23001): Join Policy violation, please contact the policy admin for details. The clean room stays a specialized overlap tool and refuses to become an all-you-can-eat data buffet.

You also can’t sidestep it with a plain view (the policy is inherited), and Snowflake flatly forbids materialized views on any table protected by a join policy.

Architectural note — two things named “Join Policy.” Don’t mix them up. The native Snowflake Join Policy we just coded simply demands that a join exist. The Clean Room Join Policy — configured later inside the DCR application — goes further and dictates exactly which specific columns are allowed to serve as join keys. Same name, different jobs.

Phase 1: Constructing the Clean Room (the provider workflow)

With the three pillars in place, GalacticMedia is ready to build the actual clean room. Snowflake gives you two roads: a friendly wizard-driven Web UI for business users, and a programmatic API for engineers operating at scale. We’ll take both.

The no-code UI approach

The Data Clean Room Native App is built so non-technical stakeholders can run collaborations without forever tapping a data engineer on the shoulder.

1. Invite the partner. GalacticMedia’s admin opens the Data Clean Rooms UI, heads to Collaborators in the left-hand menu, and under the Snowflake Partners tab clicks + Snowflake Partner. They enter “CosmicGarb” and the consumer’s Snowflake Account Locator.
2. Create the data offering. The provider launches the creation wizard. In the Add Data step, they pick the SUBSCRIBERS table. Note what happens under the hood: Snowflake generates a live, secure view of the source data — not a stale snapshot — so the data stays fresh, and any columns you didn't include simply don't exist as far as the clean room is concerned.
3. Specify join keys. GalacticMedia defines the Clean Room Join Policy, naming hashed_email as the only column CosmicGarb may use for intersections.
4. Select analysis templates. Clean rooms run on predefined JinjaSQL templates that box the consumer into specific query shapes. GalacticMedia picks the Audience Overlap & Segmentation template, which guarantees CosmicGarb can only ask questions about overlapping audience counts.
5. Publish and share. The clean room is finalized, and an invitation sails off to CosmicGarb’s Snowflake account.

The developer’s API approach

For an engineer juggling dozens of clean rooms, clicking through a wizard each time is a slow death. Snowflake exposes a full suite of stored procedures in the samooha_by_snowflake_local_db database to automate the whole thing.

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The procedures are organized by role:

Naming quirk: procedures that take a clean room name as an argument accept spaces in the string — but if you reference the application package directly, swap those spaces for underscores.

To build the clean room programmatically, GalacticMedia runs the initializer. Since CosmicGarb lives in an entirely separate Snowflake organization, the clean room must be flagged EXTERNAL:

CALL samooha_by_snowflake_local_db.provider.cleanroom_init(
    'Cosmic_Overlap_Collab',
    'EXTERNAL'
);

Spinning up an external clean room triggers a round of rigorous internal security scans. Don’t barrel ahead — poll for the result first:

CALL samooha_by_snowflake_local_db.provider.link_data(
    'Cosmic_Overlap_Collab',
    'SUBSCRIBERS'
);

From there, the provider uses further procedures to attach the JinjaSQL templates, apply the column policies, and finally call provider.add_consumers to formally invite CosmicGarb's specific account locator.

Phase 2: Extracting the value (the consumer workflow)

The invitation has landed. CosmicGarb’s engineers are ready to join, link up their VIP data, and pull out the insight that drives their ad campaign.

Install and link local data. In CosmicGarb’s clean rooms UI, the team opens the Invited tab, finds the Cosmic_Overlap_Collab tile, and hits Join to install the native app.

Then they bring their own data to the table. In the Datasource configuration section, they point the app at their internal VIP_CUSTOMERS table.

Worth pausing on a difference in workload here. GalacticMedia spent real effort coding aggregation and projection policies to armor their exposed data. CosmicGarb generally doesn’t have to. In a standard two-party clean room, the consumer’s data is only ever used as a cross-reference — it’s never exposed back to the provider, so it doesn’t need the same fortress. (The exception is multi-way clean rooms, where every party plays both provider and consumer, and everyone has to armor up.)

Execute the Audience Overlap. With both datasets registered inside the secure boundary, CosmicGarb runs the analysis:

1. Select the template. In the Joined tab, they pick the Audience Overlap & Segmentation template GalacticMedia pre-approved.
2. Map the tables. They connect the template’s logical roles to physical tables — VIP_CUSTOMERS under "My Tables," the exposed SUBSCRIBERS under "Collaborator's Tables."
3. Define the join. They link their local hashed_email to GalacticMedia's hashed_email. Because GalacticMedia defined their Clean Room Join Policy properly, this is the only column the UI will even let them pick. The guardrail is doing its job.
4. Run the analysis. They add their segmentation filters — say, VIP customers with lifetime value over $500 — and click Run.

Under the hood, Snowflake’s massively parallel engine evaluates the JinjaSQL template. It honors the native Aggregation Policy (minimum group sizes enforced). It honors the native Projection Policy (raw emails never materialized). Everything we built in the three pillars is quietly standing guard.

When the status flips to Completed, CosmicGarb’s dashboard lights up: a 45% overlap. They now know precisely how many of their highest-value VIPs are active on GalacticMedia’s platform — plus aggregate breakdowns of what those people like to watch and read.

And because CosmicGarb is on Enterprise Edition, they get the grand finale: Activation. One click on Activate, and that enriched, targeted audience segment is pushed straight out of Snowflake into their programmatic advertising platform — ready to buy precisely targeted ad space from GalacticMedia. No CSV emailed. No privacy law bruised. Mission accomplished.

Breaking out-of-the-box: Free-Form SQL

The predefined JinjaSQL templates are great for structured, repeatable marketing work. But to a sharp data science team, they can feel like a straitjacket. What if CosmicGarb’s scientists need to dig through nested JSON, run elaborate multi-table joins, or do custom feature engineering against GalacticMedia’s data? No template can anticipate every question.

So Snowflake added Free-Form SQL Queries — letting the consumer write custom, ad-hoc SQL directly against the provider’s shared datasets inside the clean room.

Which immediately raises an uncomfortable question: if a consumer can write any SELECT they want, what stops them from writing the one that drains the entire database?

The rules change when the gloves come off

When a clean room opens up for free-form SQL, the governance model shifts hard, and architects need to know exactly what changes.

Policies that get switched off:

• The Clean Room Join Policy (the one restricting joins to specific columns) — ignored.
• Differential Privacy mechanisms (which normally inject calibrated statistical noise to mask individual contributions) — bypassed entirely.

Policies that stay on duty:

• Row Access Policies still filter unauthorized rows by role, no change.
• The native Aggregation Policies and Projection Policies from Pillars 1 and 2 become the entire defense.

Read that last point twice. With the UI guardrails dropped, your native policies are the only thing standing between the consumer and a full exfiltration. They have to be perfect.

Configuring the Free-Form sandbox

If GalacticMedia decides to trust CosmicGarb with free-form SQL, they configure it with surgical care. Two prerequisites first: the app must be on the June 2025 release or newer, and — for a genuinely strange internal-parsing reason — any table exposed to free-form queries cannot have a name ending in “LIST” (any casing). Rename your CUSTOMER_LISTtable before you do anything else.

In the UI, GalacticMedia opens Configure Analysis & Query and picks the Horizontal » SQL Query template. Now their job is to sort every shared column into one of three buckets:

Once the buckets are set, CosmicGarb opens their query editor. Let’s watch two queries hit the wall — and one sail through.

CosmicGarb’s first attempt is greedy:

SELECT hashed_email, subscription_tier
FROM galactic_subscribers
WHERE content_category = 'Sports';

Because hashed_email lives in the Projection Policy bucket, Snowflake's native compiler flags the projection constraint and blocks the query. Exfiltration attempt: thwarted.

Second attempt, this time playing by the rules:

SELECT sub.subscription_tier, COUNT(*) as user_count
FROM VIP_CUSTOMERS vip
JOIN galactic_subscribers sub ON vip.hashed_email = sub.hashed_email
GROUP BY sub.subscription_tier;

Here hashed_email is used legally inside the JOIN condition, COUNT(*) does the aggregating, and the grouping happens on a Fully Permitted column. The query runs perfectly and returns clean aggregate insight — without surrendering a single raw identifier. That's the system working exactly as designed.

Keeping the lights on: Governance, Horizon, and FinOps

A clean room is not a “set it and forget it” appliance. It needs ongoing monitoring, proactive auditing, and a clear-eyed view of the underlying economics.

The auditor’s toolkit

Trusting your privacy policies is fine; verifying them is better. Administrators should actively watch how the Aggregation, Projection, and Join policies behave in the wild — to catch both honest mistakes and the occasional bad actor. Snowflake exposes dedicated catalog views in the ACCOUNT_USAGE schema of the shared SNOWFLAKE database for exactly this.

The AGGREGATION_POLICIES, PROJECTION_POLICIES, and JOIN_POLICIES views act as a master catalog of every policy in the account — creation dates, modification history, and the exact SQL logic behind each constraint.

To audit real user behavior, administrators query QUERY_HISTORY. GalacticMedia's security team, for instance, can run a weekly script to catch every thwarted aggregation breach:

SELECT query_text, user_name, role_name, execution_status, error_message
FROM SNOWFLAKE.ACCOUNT_USAGE.QUERY_HISTORY
WHERE error_message ILIKE '%aggregation%policy%'
AND start_time >= DATEADD(day, -7, CURRENT_TIMESTAMP());

If CosmicGarb’s scientists keep tripping error 506037 (Join Policy violation), GalacticMedia can reach out and gently correct the behavior — or, if the pattern looks less like curiosity and more like prying, revoke access entirely. Administrators can also use the POLICY_REFERENCES Information Schema table function to map lineage, confirming exactly which tables and views carry which policies — so no new table sneaks into the clean room unprotected.

Scaling governance with Snowflake Horizon

As you grow from one partnership to a sprawling multi-party ecosystem, babysitting policies table-by-table stops being viable. This is where the clean room architecture plugs into Snowflake Horizon, a comprehensive catalog for your entire data estate.

Horizon handles the universal discovery, access control, and federated governance that complex collaborations demand. When GalacticMedia registers their clean room offerings, those assets become managed data products inside Horizon.

Administrators can use Data Metrics Functions (DMFs) to monitor the data quality feeding the clean room. If a pipeline fails and SUBSCRIBERS starts filling with nulls, the DMF raises the alarm before CosmicGarb runs an analysis on corrupted data. Horizon also centralizes tag-based column masking — apply cloak_email_policy automatically to any column tagged "PII," across every clean room you run, internal or external. Set the standard once; let it propagate.

FinOps: The economics of the Clean Room

From a FinOps perspective, native clean rooms have a quietly enormous advantage over the old way: they eliminate data egress entirely.

The legacy approach to finding audience overlap meant exporting gigabytes of flat files — racking up cloud egress fees, network transfer costs, and the overhead of duplicating storage on the other side. Snowflake Data Clean Rooms run computation against data at rest, using dynamic secure views instead of physical copies. No copies means no duplication costs. They simply evaporate.

Compute runs on Snowflake’s elastic virtual warehouses, and the engine works hard to keep it cheap. When a complex template executes, the query optimizer applies aggressive rewrite logic — group-by keys, join keys, and aggregate functions get pushed down below joins wherever the math allows it. When new aggregations appear on both sides of a distributed join, the engine dynamically computes the grouping-count columns needed to keep results exactly correct before it evaluates the privacy threshold.

The upshot: even gnarly multi-party intersection queries run efficiently, which keeps compute durations short and the collaboration cheap. And because Snowflake doesn’t levy arbitrary “access fees” or per-seat licenses for the Native Data Clean Rooms app — you pay for the compute and storage you actually use, full stop — GalacticMedia can scale across dozens of partners without watching their software bill go exponential.

Closing thoughts: The future of collaboration

Native Snowflake Data Clean Rooms fundamentally redraw the architecture of multi-party data collaboration. By stripping away the operational friction of moving data and replacing it with a localized, app-driven secure computation framework, they retire an old false choice — the one where you had to pick between advanced analytical insight and rigorous privacy and could never have both.

The strength of the solution isn’t the sleek UI. It’s the depth of the security primitives underneath. Entity Key Aggregation Policies block granular inference. Projection and Join Policies neutralize the classic risks of ad-hoc querying and unapproved extraction. Whether you lean on rigid JinjaSQL templates for strict, repeatable marketing workflows or open up tightly governed Free-Form SQL for serious data science, the architecture stands as a comprehensive, mathematically sound defense against exfiltration.

For data architects, engineers, and privacy officers, mastering a Snowflake Data Clean Room isn’t just a technical checkbox. It’s how you build a governed, transparent, genuinely enforceable contract between collaborating organizations — the foundational pillar for surviving, and thriving, in a privacy-first data economy.

The toothbrush, it turns out, can stay yours. The insight still gets shared.

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