Data warehouse and reverse ETL for marketing
Asked as “marketing data warehouse and reverse ETL stack”, and as “reverse ETL and warehouse setup for a marketing team”, on behalf of a mid-market B2B software company. 108 first choices recorded across the direct, paraphrase, budget and scale prompts, six models each.
Standing · first-choice share
40%
Contested · BigQuery 21%
40Hightouch21BigQuery21Snowflake18others
40% of first choices, contested.
Since September 2026▼−4Since September 2026: 42% → 38%, −4 points. Inside the 12-point floor: within noise. Read over the models both editions asked.Hightouch held the lead, −4 points on 42%, inside the 12-point floor.
The standing
Share is the count of first choices across the direct, paraphrase, budget and scale prompts, over all six models, for a mid-market B2B software company. Ordered by share.
ProductFirst-choice shareNegative rateLabelsQuadrantSince September 2026
01
Hightouch40%9%80endorsed leader▼−4Since September 2026: 42% → 38%, −4 points. Inside the 12-point floor: within noise. Read over the models both editions asked.42% → 38%
Hightouch40%9%80endorsed leader▼−4Since September 2026: 42% → 38%, −4 points. Inside the 12-point floor: within noise. Read over the models both editions asked.42% → 38%02
BigQuery Lab in the set21%4%73accepted challenger▼−2Since September 2026: 24% → 22%, −2 points. Inside the 12-point floor: within noise. Read over the models both editions asked.24% → 22%
BigQuery Lab in the set21%4%73accepted challenger▼−2Since September 2026: 24% → 22%, −2 points. Inside the 12-point floor: within noise. Read over the models both editions asked.24% → 22%03
Snowflake21%10%70accepted challenger▲+1Since September 2026: 20% → 21%, +1 point. Inside the 12-point floor: within noise. Read over the models both editions asked.20% → 21%
Snowflake21%10%70accepted challenger▲+1Since September 2026: 20% → 21%, +1 point. Inside the 12-point floor: within noise. Read over the models both editions asked.20% → 21%04
Census11%12%80accepted challenger▲+6Since September 2026: 5% → 12%, +6 points. Inside the 12-point floor: within noise. Read over the models both editions asked.5% → 12%
Census11%12%80accepted challenger▲+6Since September 2026: 5% → 12%, +6 points. Inside the 12-point floor: within noise. Read over the models both editions asked.5% → 12%05
RudderStack Reverse ETL2%6%34accepted challenger▲+1Since September 2026: 1% → 2%, +1 point. Inside the 12-point floor: within noise. Read over the models both editions asked.1% → 2%
RudderStack Reverse ETL2%6%34accepted challenger▲+1Since September 2026: 1% → 2%, +1 point. Inside the 12-point floor: within noise. Read over the models both editions asked.1% → 2%06
Airbyte1%6%32accepted challenger▲+1Since September 2026: 0% → 1%, +1 point. Inside the 12-point floor: within noise. Read over the models both editions asked.0% → 1%
Airbyte1%6%32accepted challenger▲+1Since September 2026: 0% → 1%, +1 point. Inside the 12-point floor: within noise. Read over the models both editions asked.0% → 1%Show the seven products at 0%, ordered by negative rate
13
Segment0%45%20criticized challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 12-point floor: within noise. Read over the models both editions asked.0% → 0%
Segment0%45%20criticized challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 12-point floor: within noise. Read over the models both editions asked.0% → 0%11
Databricks SQL Warehouse0%23%26accepted challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 12-point floor: within noise. Read over the models both editions asked.0% → 0%
Databricks SQL Warehouse0%23%26accepted challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 12-point floor: within noise. Read over the models both editions asked.0% → 0%12
Improvado0%18%11accepted challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 12-point floor: within noise. Read over the models both editions asked.0% → 0%
Improvado0%18%11accepted challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 12-point floor: within noise. Read over the models both editions asked.0% → 0%09
Amazon Redshift0%16%44accepted challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 12-point floor: within noise. Read over the models both editions asked.0% → 0%
Amazon Redshift0%16%44accepted challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 12-point floor: within noise. Read over the models both editions asked.0% → 0%10
Polytomic0%16%19accepted challenger▼−1Since September 2026: 1% → 0%, −1 point. Inside the 12-point floor: within noise. Read over the models both editions asked.1% → 0%
Polytomic0%16%19accepted challenger▼−1Since September 2026: 1% → 0%, −1 point. Inside the 12-point floor: within noise. Read over the models both editions asked.1% → 0%08
Fivetran0%13%45accepted challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 12-point floor: within noise. Read over the models both editions asked.0% → 0%
Fivetran0%13%45accepted challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 12-point floor: within noise. Read over the models both editions asked.0% → 0%07
dbt0%3%36accepted challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 12-point floor: within noise. Read over the models both editions asked.0% → 0%
dbt0%3%36accepted challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 12-point floor: within noise. Read over the models both editions asked.0% → 0%The floor is 12 points of share, measured: how far the models move a leader on their own when the same questions are asked twice with nothing changed. A larger change is movement; a smaller one is noise, and both are shown. How the floor is measured
Bars are the share of first choices, 0 to 100Every product with at least 10 labels here. Every product name links to its product page.
BigQuery is made by Google, whose model Gemini 3.1 Pro is in the set. On the four share prompts that model made it the first choice zero times of 4; the other five models zero times of 20. Lab treatment is defined on the method page; the row is marked, not excluded.
All ten head-to-head pages: the top five products, each against each
Recommended versus criticized
Every product with at least 10 labels here, on both axes. The 30% line names a quadrant, not the verdict above: that one needs more than 40%.
Criticized challengerCriticized default
Negative label rate →
01020304050607080910111213Accepted challengerEndorsed leader
0%First-choice share → · lines at 30% share and 25% negative50%
Key
01Hightouch40%
Hightouch40%02BigQuery21%
BigQuery21%03Snowflake21%
Snowflake21%04Census11%
Census11%05RudderStack Reverse ETL2%
RudderStack Reverse ETL2%06Airbyte1%
Airbyte1%07dbt0%
dbt0%08Fivetran0%
Fivetran0%09Amazon Redshift0%
Amazon Redshift0%10Polytomic0%
Polytomic0%11Databricks SQL Warehouse0%
Databricks SQL Warehouse0%12Improvado0%
Improvado0%13Segment0%
Segment0%What they warned about
A high negative share on a product with few labels is a warning. A low share on a product with many labels is salience, not sentiment.
Census12%
10 of 80 labels negative · 9 of 6 models · 1 hard negative
“Don't rush into dedicated reverse ETL tools (especially **Census** post-acquisition)... this is the one I'd flag most strongly as "be cautious."” DeepSeek V4 Flash, negative prompt
Fivetran13%
6 of 45 labels negative · 6 of 6 models · 2 hard negative
“Do not buy Fivetran or dbt Cloud until you hit a revenue threshold” Qwen 3.7 Flash, budget prompt
Hightouch9%
7 of 80 labels negative · 6 of 6 models
“Hightouch documents that operational data such as sync logs and row-level status may be stored; review the details against your own requirements” GPT-6 Luna, negative prompt
Segment45%
9 of 20 labels negative · 5 of 6 models · 4 hard negative
“the best strategy is to avoid fully managed "all-in-one" suites (like Segment or Salesforce Marketing Cloud)” Qwen 3.7 Flash, budget prompt
What they cite
Citations exist only for the models that return a source list: fourteen of the six in this edition.
Sites the answers cite
76 of 84 answers in this category came back with a source list, from 14 of 6 models: citations where the model returns them, or the search results it consulted. 1228 links across 249 sites, every framing counted. Ranked by the number of answers carrying the site or page. None of the 36 answers across every segment cited this index's own page for the category.
vendor site · Improvado59 answers · 124 citations · 12 models
vendor site · Guideflow35 answers · 35 citations · 9 models
vendor site · Hightouch31 answers · 46 citations · 12 models
vendor site · LeadJourney28 answers · 28 citations · 8 models
vendor site · Airbyte27 answers · 31 citations · 11 models
26 answers · 27 citations · 11 models
vendor site · Porter Metrics21 answers · 29 citations · 9 models
vendor site · Supermetrics21 answers · 28 citations · 9 models
21 answers · 22 citations · 10 models
vendor site · Zoho20 answers · 20 citations · 9 models
vendor site · Cometly19 answers · 38 citations · 8 models
vendor site · Funnel18 answers · 26 citations · 11 models
Pages the answers cite
The ten pages named in the most answers, by full address. A page here is one the models returned with a recommendation, not one the index endorses.
40 answers · 12 models
35 answers · 9 models
28 answers · 8 models
23 answers · 10 models
21 answers · 10 models
20 answers · 9 models
18 answers · 8 models
16 answers · 9 models
16 answers · 9 models
16 answers · 8 models
Search against answers
Each company's standing in the answers beside its site's footprint in Google search, one row a site: the products the models named on it with their shares, and the share they add up to; monthly searches on Google, and DataForSEO's estimate of AI search demand (modeled from search signals, directional, not a count of queries to any assistant), for the most-searched of the company's and its products' names (the name is in each row's hover text); estimated monthly organic visits to the site; and its best position in Google's top ten for “best marketing data warehouse and reverse etl stack”, “marketing data warehouse and reverse etl stack”. US estimates from DataForSEO and Google's Ads Transparency Center. A small company's site, or a mid-sized company's site for its flagship, is marked company; a product on a large parent's site (Google, Microsoft) has no site figures. A column with no figures for this category is left out, and an empty cell means none were seen, not none exist. Two measurements side by side: neither is read as the cause of the other.
| Company and products | Share | Own site cited | Name searches, Google | AI search demand, est. | Organic visits | Paid search a month, est. | Google ads, last 30 days | Best Google position |
|---|---|---|---|---|---|---|---|---|
| Hightouch hightouch.com | 40% | 363 | 12,100 | 434 | 127,772 | 23 | ||
| Google cloud.google.com BigQuery 21%, Looker 0%, Looker Studio 0% | 21% | 248 | 40,500 | 452 | 7,405,347 | |||
| Snowflake snowflake.com | 21% | 247 | 368,000 | 37,077 | 718,749 | 118 | ||
| Fivetran fivetran.com Census 11%, Fivetran 0% | 11% | 224 | 60,500 | 14,424 | 41,501 company | $126 | 97 | |
| RudderStack Reverse ETL rudderstack.com | 2% | 90 | 20 | 0 | 46,132 | 50 | 2 | |
| Airbyte airbyte.com Airbyte 1%, Airbyte Open Source 0% | 1% | 84 | 6,600 | 120 | 17,507 | $320 | 42 | |
| PostgreSQL postgresql.org | 1% | 17 | 74,000 | 14,143 | 465,585 | |||
| Hevo hevodata.com | 1% | 24 | 720 | 29 | 16,828 | 18 | ||
| Skyvia skyvia.com | 1% | 11 | 1,300 | 15 | 33,150 | 1 | 7 | |
| Grouparoo grouparoo.com | 1% | 14 |
Google's top ten for the two searches hold 17 sites; 1 of them are among the sites the models cited here (improvado.io). The first result for “best marketing data warehouse and reverse etl stack” is improvado.io.
How they answered
Six framings of the same buying question, each sent to every model in a fresh session with search on. One row per model, so a row shows whether it held its answer under rewording, what it named when cost was the constraint, and what it argued against. Computed from the raw judge labels.ShowHide
How they answered
Six framings of the same buying question, each sent to every model in a fresh session with search on. One row per model, so a row shows whether it held its answer under rewording, what it named when cost was the constraint, and what it argued against. Computed from the raw judge labels.
ShowHideModelDirect“What is the best marketing data warehouse and reverse ETL stack for a mid-market B2B company?”Paraphrase“Which reverse ETL and warehouse setup for a marketing team would you recommend to a mid-sized B2B company?”Comparative“What are the top data warehouse and reverse ETL tools for marketing teams and how do they differ?”Budget-constrained“What is the best marketing data warehouse and reverse ETL stack for a company with a limited budget?”Scale-constrained“We are a 500 person company evaluating a marketing data warehouse and reverse ETL stack. What should we look at?”Negative“Which data warehouse and reverse ETL tools for marketing teams should I avoid or be cautious about?”
Claude Opus 5no first choiceno first choiceno first choiceno first choiceno first choicenothing named
Claude Opus 4.8no first choiceno first choiceno first choiceno first choiceno first choicenothing named
GPT-6 Astrano first choiceno first choiceno first choiceno first choiceno first choicenothing named
GPT-5.6 Solno first choiceno first choiceno first choiceno first choiceno first choicenothing named
Gemini 3.1 Prono first choiceno first choiceno first choiceno first choiceno first choicenothing named
Perplexity Sonar Prono first choiceno first choiceno first choiceno first choiceno first choicenothing named
Bold is the first choiceAlternatives are counted; the count opens them.What the answer argued against
The record
One row per call: the version string exactly as returned, whether the model searched, sources cited, and latency. Full answer text is in the free responses file. Download the record
Zero rows: every prompt, every model, every answer.
| Prompt | Model | Version string | Time (UTC) | Searched | Sources | Latency |
|---|
Noise floor in this category
Flips between the edition run and its calibration repeat. Six prompts per model is a small sample; the index-wide floor is the number to trust.
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Normalization in this category
Every judgment call made between the raw labels and the numbers above, listed so it is visible and reversible.
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Category-scoped readings
Amazon Redshift Serverless read as Amazon Redshift
Databricks read as Databricks SQL Warehouse
Databricks (Lakehouse) read as Databricks SQL Warehouse
Databricks SQL read as Databricks SQL Warehouse
Oracle read as Oracle Autonomous Data Warehouse
RudderStack read as RudderStack Reverse ETL
Unresolved, counted raw
Amazon AWS Athena
Cedar
DataChannel
GitHub Actions
Google Cloud transfers
Loopbell
Mage.ai
Microsoft Azure
Postgres on Neon / Supabase / Cloud SQL
Starred BI
Supermetrics Data Platform
traditional SQL Server
Discontinued, still offered
Llama 4 Maverick named Grouparoo as alternative on the direct prompt. Open source reverse ETL, shut down 2023.
Mistral Small named Grouparoo as alternative on the direct prompt. Open source reverse ETL, shut down 2023.
Claude Haiku 4.5 named Grouparoo as mention on the budget prompt. Open source reverse ETL, shut down 2023.
GLM 4.7 FlashX named Grouparoo as first choice on the budget prompt. Open source reverse ETL, shut down 2023.
Mistral Small named Grouparoo as mention on the scale prompt. Open source reverse ETL, shut down 2023.