Index › Products › Google BigQuery Data Clean Rooms · October 2026 Edition

Google · 1 category · Named, not ranked

Google BigQuery Data Clean Rooms

12Judge labels

3First choices

0Negative labels

6 / 14Models named it

1Category

October 2026 Edition. Every number here is derived from the raw labels under vendor table v2026-10-05.2, every buyer segment counted.

Standing

1 label, too few to rank

A product needs 10 labels in a category before a share or quadrant is stated. Google BigQuery Data Clean Rooms was named 1 time in Data clean rooms, where Snowflake Data Clean Rooms led with 19%. The labels and the evidence are below, counted exactly.

By buyer segmentRead the same way at every buyer size.

Small business6 of 107

under the label threshold

Mid-market41 of 100

under the label threshold

Enterprise13 of 75

under the label threshold

In data clean rooms · each standing computed within its segment · bars are 0 to 100 · the accent bar is the product's own best reading

Standing by category

Every category where a model named Google BigQuery Data Clean Rooms for a mid-market B2B company. Share is first choices across the direct, paraphrase, budget and scale prompts; rank is within every product named in that category.

Category	Function	Share	Rank	Negative rate	Labels	Quadrant	Since September 2026
Data clean rooms	GTM data and infrastructure	0%	41 of 100	0%	1	under 10 labels · led by Snowflake Data Clean Rooms at 19%

Movement

This is the first edition on this tier, so no move can be computed for Google BigQuery Data Clean Rooms yet. From the next edition this section shows, per buyer segment and per category, whether its share moved by more than the measured noise floor.

By model

How each model treated Google BigQuery Data Clean Rooms across every prompt where it was named for a mid-market B2B company. Fourteen models, six prompts per category.

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Model	Alternative	Labels
Claude Haiku 4.5	0	0
GPT-5.4 mini	0	0
Gemini 3.5 Flash	0	0
Perplexity Sonar	0	0
Grok 4.1 Fast	0	0
Mistral Small	0	0
DeepSeek V4 Flash	0	0
Llama 4 Maverick	0	0
Qwen 3.7 Flash	0	0
Kimi K2	0	0
GLM 4.7 FlashX	0	0
MiniMax M2.5	1	1
GPT-6 Luna	0	0
Muse Glimmer 30B	0	0

By framing

Which of the six questions produced the naming. By model says how often; this says asked what. The first-choice count on the right carries the marks of the models that produced it.

FramingLabels by classFirst choices

Direct2 labels1

Paraphrase1 labelNone

Comparative4 labelsNone

Budget-constrained4 labels1

Scale-constrained1 label1

Negative0 labelsNone

First choiceAlternativeMentionNegative12 labels in all, every segment counted; 3 of the 3 first choices count toward share, since the comparative and negative framings do not. The bar is one segment per label class, to scale within the framing.

What the models said for it

Verbatim evidence the judge attached to positive labels.

“Offers $300 in free credits to start, then pay-per-use” MiniMax M2.5 · Data clean rooms · budget prompt · alternative

And against it

Verbatim evidence attached to negative labels. A warning on a product with few labels is a warning; on a product with many, it is one voice among them.

No model argued against it.

Named alongside

The products named in the same answers as Google BigQuery Data Clean Rooms, over the 12 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Google BigQuery Data Clean Rooms was named but was not.

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ProductSame answerTook the first choice insteadHead to head

AWS Clean Rooms11 of 123Not among the top eight

Snowflake Data Clean Rooms11 of 123Not among the top eight

InfoSum6 of 120Not among the top eight

LiveRamp Clean Room5 of 121Not among the top eight

Amazon Marketing Cloud4 of 121Not among the top eight

Google Ads Data Hub3 of 121Not among the top eight

Databricks Clean Rooms3 of 120Not among the top eight

Decentriq3 of 120Not among the top eight

Habu3 of 120Not among the top eight

Optable3 of 120Not among the top eight

A head-to-head page exists where both products are among a category's top eight. The other rows are the same fact without a page behind them, so they link to the product instead.

What carried it into the answer

The sites and pages cited by the answers that named Google BigQuery Data Clean Rooms. A fact about retrieval, not a lever on the model.

Citations exist only for the models that return a source list, five of the fourteen in this edition, so these counts come from 12 of the 12 answers that named Google BigQuery Data Clean Rooms and are not a share of its labels.

Domains cited

us.fitgap.com9

sourceforge.net8

aiopsschool.com7

aws.amazon.com7

digitalapplied.com7

guideflow.com7

marketintelo.com7

itechguides.com6

docs.snowflake.com5

gartner.com5

Sixty-eight of the sixty-eight domain citations in answers naming Google BigQuery Data Clean Rooms came from somebody else's page.

Pages cited

aiopsschool.com/blog/top-10-data-clean-room-platforms-for-ai-features-pros-cons-comparison7

digitalapplied.com/blog/data-clean-rooms-advertising-2026-marketer-decision-guide7

guideflow.com/blog/data-clean-room-software7

sourceforge.net/software/data-clean-room7

us.fitgap.com/search/data-clean-room-software/small-business7

aws.amazon.com/clean-rooms/pricing5

gartner.com/reviews/market/data-clean-rooms5

marketintelo.com/report/data-clean-rooms-market5

Pages are listed as the models cited them.

Follow Google BigQuery Data Clean Rooms

An email the morning each edition publishes: where this product moved, where it held, and by how much against the noise floor. One address, confirmed by a click; a stop link in every email.

Already following? Everything you follow, with a stop for each.

The company

Google is the company behind Google BigQuery Data Clean Rooms.

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On record

Wikidata

Website: google.com
Headquarters: Mountain View, California, United States
Founded: 1998
Ownership: Part of Alphabet Inc.
Also from Google: Adometry, Attribution 360, BigQuery, Firebase, Google ADH, Google Ad Manager, Google AdMob, Google AdSense and 81 more

Fetched September 14, 2026, with a correction applied. These describe the company, not the product's standing, and a claimed page can dispute any of them. · Wikidata

Is this your product?

Claim this page

Claiming is free and changes nothing in the data. A claimed page shows a verified contact who is told when each edition publishes and when Google BigQuery Data Clean Rooms's standing changes by more than the noise floor; the right to propose corrections to the vendor table, meaning names the judge wrote that should or should not read as Google BigQuery Data Clean Rooms, applied by version and listed in the change log; and a one-line description supplied by the vendor and marked as such.

What a new claim receivesHide what a new claim receives

A new claim receives the current edition's vendor brief for Google BigQuery Data Clean Rooms by email, built from the raw record of the edition. It shows:

where Google BigQuery Data Clean Rooms is named, by buyer and by framing, and which cells hold its first choices;
the claims the models make when they name it, ranked, with the strongest and the weakest quoted;
its vocabulary against the segment leader's, and the pages the models cited;
who was chosen in the answers that did not name Google BigQuery Data Clean Rooms, and every reason the record gives;
a battlecard for each top rival: the head-to-head split, why they win, and the reservation quoted against them;
one page of published figures cleared to show a buyer.