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. 43 first choices recorded across the direct, paraphrase, budget and scale prompts, six models each.
Standing · first-choice share
40%
Contested · BigQuery 26%
40Hightouch26BigQuery23Snowflake12others
40% of first choices, contested.
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
02
BigQuery Lab in the set26%9%35accepted challengernewNew since September 2026: not ranked then, 26% now.
BigQuery Lab in the set26%9%35accepted challengernewNew since September 2026: not ranked then, 26% now.05
RudderStack Reverse ETL2%6%18accepted challengernewNew since September 2026: not ranked then, 2% now.
RudderStack Reverse ETL2%6%18accepted challengernewNew since September 2026: not ranked then, 2% now.Show the six products at 0%, ordered by negative rate
11
Databricks SQL Warehouse0%44%16criticized challengernewNew since September 2026: not ranked then, 0% now.
Databricks SQL Warehouse0%44%16criticized challengernewNew since September 2026: not ranked then, 0% now.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 can't be told apart from repeat variation, and both are shown. Each index measures its own floor from its own repeat, over its own categories, so the floor differs from one index to the next. 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 two times of 4; the other five models nine 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 →
0102
03
0405
06
07
08
09
1011Accepted challengerEndorsed leader
0%First-choice share → · lines at 30% share and 25% negative50%
Key
01Hightouch40%
Hightouch40%02BigQuery26%
BigQuery26%03Snowflake23%
Snowflake23%04Census9%
Census9%05RudderStack Reverse ETL2%
RudderStack Reverse ETL2%06dbt0%
dbt0%07Airbyte0%
Airbyte0%08Fivetran0%
Fivetran0%09Segment0%
Segment0%10Amazon Redshift0%
Amazon Redshift0%11Databricks SQL Warehouse0%
Databricks SQL Warehouse0%What they warned about
One of six models held their first choice under the paraphrase. Claude Opus 4.8, GPT-6 Astra, GPT-5.6 Sol, Gemini 3.1 Pro and Perplexity Sonar Pro changed. 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.
Fivetran26%
7 of 27 labels negative · 5 of 6 models · 1 hard negative
“Avoid Snowflake, Fivetran and enterprise CDP packages at the beginning” GPT-5.6 Sol, budget prompt
Census15%
5 of 34 labels negative · 5 of 6 models
“if you're a standalone reverse ETL customer with no Fivetran ELT footprint, ask hard questions about roadmap, renewal pricing, and whether the standalone SKU survives long-term” Claude Opus 5, negative prompt
Amazon Redshift39%
7 of 18 labels negative · 4 of 6 models · 1 hard negative
“Avoid provisioned Redshift for a small, lightly staffed team.” GPT-5.6 Sol, negative prompt
Snowflake19%
6 of 32 labels negative · 4 of 6 models · 1 hard negative
“Avoid Snowflake, Fivetran and enterprise CDP packages at the beginning” GPT-5.6 Sol, budget prompt
What they cite
Six of the six models returned a source list in this edition: six return the pages they cited.
Sites the answers cite
30 of 36 answers in this category came back with a source list, from 6 of 6 models: citations where the model returns them, or the search results it consulted. 508 links across 181 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 · Hightouch21 answers · 33 citations · 5 models
vendor site · Fivetran14 answers · 23 citations · 5 models
vendor site · Improvado14 answers · 21 citations · 4 models
vendor site · Integrate11 answers · 20 citations · 4 models
10 answers · 10 citations · 4 models
vendor site · Google9 answers · 9 citations · 2 models
vendor site · DinMo8 answers · 10 citations · 4 models
vendor site · Funnel8 answers · 10 citations · 4 models
vendor site · Polytomic8 answers · 10 citations · 2 models
vendor site · RudderStack7 answers · 9 citations · 2 models
vendor site · Definite7 answers · 8 citations · 4 models
vendor site · Fastero7 answers · 7 citations · 2 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.
8 answers · 3 models
8 answers · 3 models
8 answers · 3 models
8 answers · 2 models
7 answers · 2 models
7 answers · 4 models
7 answers · 2 models
6 answers · 3 models
6 answers · 3 models
6 answers · 2 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% | 399 | 12,100 | 434 | 127,772 | 23 | ||
| Google cloud.google.com BigQuery 26% | 26% | 208 | 22,200 | 2,048 | 7,405,347 | |||
| Snowflake snowflake.com | 23% | 234 | 368,000 | 37,077 | 718,749 | 118 | ||
| Fivetran fivetran.com Census 9%, Fivetran 0% | 9% | 238 | 60,500 | 14,424 | 41,501 company | $126 | 97 | |
| RudderStack Reverse ETL rudderstack.com | 2% | 122 | 20 | 0 | 46,132 | 50 | 2 | |
| dbt Labs getdbt.com dbt 0% | 0% | 122 | 75,753 | 12 | ||||
| Airbyte airbyte.com | 0% | 105 | 6,600 | 120 | 17,507 | $320 | 42 | |
| Twilio segment.com Segment 0% | 0% | 460 | 135,000 | 4,343 | 9,086 | 0 | ||
| Snowplow snowplow.io | 0% | 30 | 49,500 | 2,183 | 18,927 | 9 | ||
| Metabase metabase.com | 0% | 116 | 14,800 | 337 | 60,151 | 1 |
Google's top ten for the two searches hold 17 sites; 5 of them are among the sites the models cited here (dinmo.com, getbruin.com, improvado.io, integrate.io, rudderstack.com). 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 software company?”Paraphrase“Which reverse ETL and warehouse setup for a marketing team would you recommend to a mid-sized B2B software 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 5Hightouch, Snowflakeagainst: Adobe Real-Time CDP, Databricks SQL Warehouse, Klaviyo, Microsoft Fabric, Salesforce Data CloudHightouch, SnowflakeHeldagainst: Airbyte, Apache Airflow, Bloomreach, Databricks SQL Warehouse, Estuary, Segment, mParticleHightouchagainst: Amazon Redshift, GrouparooBigQuery, Hightouchagainst: Fivetran, Snowflake, Supermetrics, dbtno first choiceagainst: Amazon Redshift, BigQuery, Census, Grouparoo, Seekwell, Snowflake
Fourteen alternatives
Airbyte, BigQuery, Census, Elementary, Fivetran, Looker, Metabase, Metaplane, Microsoft Power BI, Omni, RudderStack Reverse ETL, Segment, Snowplow, dbtEight alternatives
BigQuery, Census, Fivetran, Omnata, Polytomic, RudderStack Reverse ETL, Snowplow, dbtFive alternatives
BigQuery, Census, Databricks SQL Warehouse, RudderStack Reverse ETL, SnowflakeSeven alternatives
Airbyte, Census, DuckDB, Multiwoven, Windsor.ai, dbt, dltClaude Opus 4.8BigQuery, Snowflakeagainst: Amazon Redshift, Databricks SQL WarehouseHightouchChangedCensus, Hightouch, SnowflakeBigQuery, Census, Hightouchagainst: Fivetran, Snowflakeno first choiceagainst: Amazon Redshift, BigQuery, Census, Hevo
Five alternatives
Airbyte, Census, Fivetran, Hightouch, dbtSix alternatives
Airbyte, BigQuery, Census, Fivetran, Snowflake, dbtFive alternatives
Airbyte, Amazon Redshift, BigQuery, Fivetran, Microsoft Azure Synapse AnalyticsThree alternatives
Airbyte, MotherDuck, dbtGPT-6 AstraHightouch, SnowflakeHightouchChangedBigQuery, Census, Hightouch, SnowflakeBigQuery, HightouchCensus, Hightouchagainst: BigQuery, Census, Grouparoo, Hightouch, Snowflake
Four alternatives
BigQuery, Census, Fivetran, dbtFive alternatives
BigQuery, Census, Fivetran, Snowflake, dbtFive alternatives
Amazon Redshift, Databricks SQL Warehouse, GrowthLoop, Microsoft Fabric, RudderStack Reverse ETLThree alternatives
Fivetran, dbt, dltFive alternatives
Airbyte, BigQuery, RudderStack Reverse ETL, Snowflake, dbtGPT-5.6 SolHightouch, Snowflakeagainst: Databricks SQL WarehouseBigQuery, HightouchChangedHightouch, SnowflakeBigQuery, RudderStack Reverse ETLagainst: Fivetran, PostgreSQL, SnowflakeBigQuery, Snowflakeagainst: Amazon Redshift, Census, Databricks SQL Warehouse, Grouparoo, Hightouch, Make, Microsoft Azure Synapse Analytics, RudderStack Reverse ETL, Snowflake, Zapier
Six alternatives
BigQuery, Census, Fivetran, RudderStack Reverse ETL, Segment, dbtFour alternatives
Census, Fivetran, Snowflake, dbtSeven alternatives
Amazon Redshift, BigQuery, Census, Databricks SQL Warehouse, Microsoft Fabric, RudderStack Reverse ETL, SegmentThree alternatives
Airbyte, Hightouch, dbtSeven alternatives
Amazon Redshift, Census, Databricks SQL Warehouse, Fivetran, Hightouch, RudderStack Reverse ETL, dbtGemini 3.1 ProCensus, Hightouch, SnowflakeHightouch, SnowflakeChangedHightouchBigQuery, Hightouchagainst: CensusBigQuery, Census, Hightouch, Snowflakeagainst: Amazon Redshift, Databricks SQL Warehouseagainst: Airbyte, Amazon Redshift, Databricks SQL Warehouse, Fivetran, Oracle Autonomous Data Warehouse, Segment, Tealium, Teradata, mParticle
Four alternatives
Airbyte, BigQuery, Fivetran, dbtFive alternatives
Airbyte, BigQuery, Census, Fivetran, dbtFour alternatives
BigQuery, Census, RudderStack Reverse ETL, SnowflakeFour alternatives
Airbyte, Fivetran, MotherDuck, dbtThree alternatives
Fivetran, RudderStack Reverse ETL, dbtPerplexity Sonar ProBigQuery, Hightouch, SnowflakeHightouchChangedagainst: FivetranBigQueryBigQueryagainst: Fivetran, PostgreSQLno first choiceagainst: Airbyte, Fivetran, Funnel, Looker, Microsoft Power BI, PostgreSQL, Stitch, Supermetrics, Tableau, Teradata
Five alternatives
Dreamdata, Fivetran, HockeyStack, Improvado, LeadJourneyThree alternatives
BigQuery, Census, SnowflakeFive alternatives
Amazon Redshift, Census, Hightouch, Microsoft Azure Synapse Analytics, SnowflakeFive alternatives
Airbyte, Census, Hightouch, Metabase, dbtBold 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.
Thirty-six rows: every prompt, every model, every answer.
| Prompt | Model | Version string | Time (UTC) | Searched | Sources | Latency |
|---|---|---|---|---|---|---|
| Direct recommendation | Claude Opus 5 | claude-opus-5 | 2026-09-08 14:23 | yes | 24 | 79 s |
| Direct recommendation | Claude Opus 4.8 | claude-opus-4-8 | 2026-09-08 14:23 | no | 0 | 28 s |
| Direct recommendation | GPT-6 Astra | gpt-6-astra | 2026-09-08 14:25 | yes | 7 | 51 s |
| Direct recommendation | GPT-5.6 Sol | gpt-5.6-sol | 2026-09-08 14:26 | yes | 7 | 61 s |
| Direct recommendation | Gemini 3.1 Pro | gemini-3.1-pro-preview | 2026-09-08 14:24 | no | 0 | 29 s |
| Direct recommendation | Perplexity Sonar Pro | sonar-pro | 2026-09-08 14:23 | yes | 20 | 14 s |
| Paraphrase | Claude Opus 5 | claude-opus-5 | 2026-09-08 14:27 | yes | 22 | 87 s |
| Paraphrase | Claude Opus 4.8 | claude-opus-4-8 | 2026-09-08 14:29 | yes | 29 | 79 s |
| Paraphrase | GPT-6 Astra | gpt-6-astra | 2026-09-08 14:30 | yes | 5 | 38 s |
| Paraphrase | GPT-5.6 Sol | gpt-5.6-sol | 2026-09-08 14:32 | yes | 5 | 74 s |
| Paraphrase | Gemini 3.1 Pro | gemini-3.1-pro-preview | 2026-09-08 14:30 | no | 0 | 28 s |
| Paraphrase | Perplexity Sonar Pro | sonar-pro | 2026-09-08 14:29 | yes | 19 | 7 s |
| Comparative | Claude Opus 5 | claude-opus-5 | 2026-09-08 14:33 | yes | 25 | 57 s |
| Comparative | Claude Opus 4.8 | claude-opus-4-8 | 2026-09-08 14:34 | yes | 35 | 71 s |
| Comparative | GPT-6 Astra | gpt-6-astra | 2026-09-08 14:36 | yes | 12 | 70 s |
| Comparative | GPT-5.6 Sol | gpt-5.6-sol | 2026-09-08 14:38 | yes | 10 | 73 s |
| Comparative | Gemini 3.1 Pro | gemini-3.1-pro-preview | 2026-09-08 14:35 | yes | 9 | 56 s |
| Comparative | Perplexity Sonar Pro | sonar-pro | 2026-09-08 14:34 | yes | 20 | 11 s |
| Budget constrained | Claude Opus 5 | claude-opus-5 | 2026-09-08 14:39 | yes | 40 | 85 s |
| Budget constrained | Claude Opus 4.8 | claude-opus-4-8 | 2026-09-08 14:40 | no | 0 | 22 s |
| Budget constrained | GPT-6 Astra | gpt-6-astra | 2026-09-08 14:42 | yes | 7 | 45 s |
| Budget constrained | GPT-5.6 Sol | gpt-5.6-sol | 2026-09-08 14:43 | yes | 7 | 57 s |
| Budget constrained | Gemini 3.1 Pro | gemini-3.1-pro-preview | 2026-09-08 14:41 | yes | 10 | 53 s |
| Budget constrained | Perplexity Sonar Pro | sonar-pro | 2026-09-08 14:40 | yes | 20 | 12 s |
| Scale constrained | Claude Opus 5 | claude-opus-5 | 2026-09-08 14:44 | yes | 24 | 79 s |
| Scale constrained | Claude Opus 4.8 | claude-opus-4-8 | 2026-09-08 14:45 | no | 0 | 33 s |
| Scale constrained | GPT-6 Astra | gpt-6-astra | 2026-09-08 14:47 | yes | 11 | 65 s |
| Scale constrained | GPT-5.6 Sol | gpt-5.6-sol | 2026-09-08 14:48 | yes | 9 | 76 s |
| Scale constrained | Gemini 3.1 Pro | gemini-3.1-pro-preview | 2026-09-08 14:46 | no | 0 | 40 s |
| Scale constrained | Perplexity Sonar Pro | sonar-pro | 2026-09-08 14:45 | yes | 20 | 14 s |
| Negative framing | Claude Opus 5 | claude-opus-5 | 2026-09-08 14:50 | yes | 35 | 91 s |
| Negative framing | Claude Opus 4.8 | claude-opus-4-8 | 2026-09-08 14:52 | yes | 32 | 155 s |
| Negative framing | GPT-6 Astra | gpt-6-astra | 2026-09-08 14:55 | yes | 6 | 49 s |
| Negative framing | GPT-5.6 Sol | gpt-5.6-sol | 2026-09-08 14:57 | yes | 10 | 82 s |
| Negative framing | Gemini 3.1 Pro | gemini-3.1-pro-preview | 2026-09-08 14:54 | yes | 8 | 101 s |
| Negative framing | Perplexity Sonar Pro | sonar-pro | 2026-09-08 14:53 | yes | 20 | 13 s |
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.
ShowHide
Claude Opus 5
3 of 5 flipped
Claude Opus 4.8
4 of 4 flipped
GPT-6 Astra
1 of 5 flipped
GPT-5.6 Sol
3 of 6 flipped
Gemini 3.1 Pro
3 of 6 flipped
Perplexity Sonar Pro
3 of 5 flipped
Normalization in this category
Every judgment call made between the raw labels and the numbers above, listed so it is visible and reversible.
ShowHide
Category-scoped readings
Azure Synapse read as Microsoft Azure Synapse Analytics
Azure Synapse dedicated SQL pools read as Microsoft Azure Synapse Analytics
Databricks 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
RudderStack Free read as RudderStack Reverse ETL
Unresolved, counted raw
None awaiting human review.
Discontinued, still offered
Perplexity Sonar Pro named Grouparoo as alternative on the negative prompt. Open source reverse ETL, shut down 2023.