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
Hightouch40% of first choices across 43, the largest first-choice pool in the edition because answers here recommend stacks rather than single products. Only Hightouch clears the leader line.
First-choice share
Recommended versus criticized
Every product with at least 10 labels here, on both axes. Leader at 30% or more of first choices; criticized at 25% or more negative labels. Markers are keyed to the table below.
Negative label rate →
25% negative
30% first choices
Criticized challenger
Criticized default
Accepted challenger
Endorsed leader
01
02
03
04
05
06
07
08
09
10
110%First-choice share →60%
Full standing
| # | Product | First choices | Negative rate | Labels | Quadrant |
|---|---|---|---|---|---|
| 01 | Hightouch | 40% | 6% | 35 | endorsed leader |
| 02 | BigQuery | 26% | 9% | 35 | accepted challenger |
| 03 | Snowflake | 23% | 19% | 32 | accepted challenger |
| 04 | Census | 9% | 15% | 34 | accepted challenger |
| 05 | RudderStack | 2% | 6% | 18 | accepted challenger |
| 06 | dbt | 0% | 4% | 23 | accepted challenger |
| 07 | Airbyte | 0% | 15% | 20 | accepted challenger |
| 08 | Fivetran | 0% | 26% | 27 | criticized challenger |
| 09 | Segment | 0% | 25% | 12 | criticized challenger |
| 10 | Amazon Redshift | 0% | 39% | 18 | criticized challenger |
| 11 | Databricks | 0% | 44% | 16 | criticized challenger |
Warned against
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
Held under rewording
Whether each model's first choice on the direct prompt survived the paraphrase in this category, under the strict rule.
Claude Opus 5
Held
Claude Opus 4.8
Changed
GPT-6 Astra
Changed
GPT-5.6 Sol
Changed
Gemini 3.1 Pro
Changed
Perplexity Sonar Pro
Changed
Noise floor in this category
Flips between the edition run and its calibration repeat (simulated until 12 September 2026). Six prompts per model is a small sample; the index-wide floor is the number to trust.
Claude Opus 5
1 of 4 flipped
Claude Opus 4.8
1 of 4 flipped
GPT-6 Astra
0 of 5 flipped
GPT-5.6 Sol
1 of 6 flipped
Gemini 3.1 Pro
1 of 6 flipped
Perplexity Sonar Pro
3 of 5 flipped
What each model said, prompt by prompt
Six framings of the same buying question, each sent to every model in a fresh session with search on. Bold is the first choice, grey the alternatives, red what the answer argued against. Computed from the raw judge labels.
| Prompt | Claude Opus 5 | Claude Opus 4.8 | GPT-6 Astra | GPT-5.6 Sol | Gemini 3.1 Pro | Perplexity Sonar Pro |
|---|---|---|---|---|---|---|
Direct recommendation What is the best marketing data warehouse and reverse ETL stack for a mid-market B2B software company? | Hightouch, Snowflake Airbyte, BigQuery, Census, Elementary +10 against: Adobe Real-Time CDP, Databricks, Klaviyo +2 | BigQuery, Snowflake Airbyte, Census, Fivetran, Hightouch +1 against: Amazon Redshift, Databricks | Hightouch, Snowflake BigQuery, Census, Fivetran, dbt | Hightouch, Snowflake BigQuery, Census, Fivetran, RudderStack +2 against: Databricks | Census, Hightouch, Snowflake Airbyte, BigQuery, Fivetran, dbt | BigQuery, Hightouch, Snowflake Dreamdata, Fivetran, HockeyStack, Improvado +1 |
Paraphrase Which reverse ETL and warehouse setup for a marketing team would you recommend to a mid-sized B2B software company? | Hightouch, Snowflake BigQuery, Census, Fivetran, Omnata +4 against: Airbyte, Airflow, Bloomreach +4 | Hightouch Airbyte, BigQuery, Census, Fivetran +2 | Hightouch BigQuery, Census, Fivetran, Snowflake +1 | BigQuery, Hightouch Census, Fivetran, Snowflake, dbt | Hightouch, Snowflake Airbyte, BigQuery, Census, Fivetran +1 | Hightouch BigQuery, Census, Snowflake against: Fivetran |
Comparative What are the top data warehouse and reverse ETL tools for marketing teams and how do they differ? | Hightouch BigQuery, Census, Databricks, RudderStack +1 against: Amazon Redshift, Grouparoo | Census, Hightouch, Snowflake Airbyte, Amazon Redshift, Azure Synapse, BigQuery +1 | BigQuery, Census, Hightouch, Snowflake Amazon Redshift, Databricks, GrowthLoop, Microsoft Fabric +1 | Hightouch, Snowflake Amazon Redshift, BigQuery, Census, Databricks +3 | Hightouch BigQuery, Census, RudderStack, Snowflake | BigQuery Amazon Redshift, Azure Synapse, Census, Hightouch +1 |
Budget constrained What is the best marketing data warehouse and reverse ETL stack for a company with a limited budget? | BigQuery, Hightouch Airbyte, Census, DuckDB, Multiwoven +3 against: Fivetran, Snowflake, Supermetrics +1 | BigQuery, Census, Hightouch Airbyte, MotherDuck, dbt against: Fivetran, Snowflake | BigQuery, Hightouch Fivetran, dbt, dlt | BigQuery, RudderStack Airbyte, Hightouch, dbt against: Fivetran, PostgreSQL, Snowflake | BigQuery, Hightouch Airbyte, Fivetran, MotherDuck, dbt against: Census | BigQuery Airbyte, Census, Hightouch, Metabase +1 against: Fivetran, PostgreSQL |
Scale constrained We are a 500 person company evaluating a marketing data warehouse and reverse ETL stack. What should we look at? | no first choice | no first choice | Census, Hightouch Airbyte, BigQuery, RudderStack, Snowflake +1 | BigQuery, Snowflake Amazon Redshift, Census, Databricks, Fivetran +3 | BigQuery, Census, Hightouch, Snowflake Fivetran, RudderStack, dbt against: Amazon Redshift, Databricks | no first choice |
Negative framing Which data warehouse and reverse ETL tools for marketing teams should I avoid or be cautious about? | no first choice against: Amazon Redshift, BigQuery, Census +3 | no first choice against: Amazon Redshift, BigQuery, Census +1 | no first choice against: BigQuery, Census, Grouparoo +2 | BigQuery Microsoft Fabric against: Amazon Redshift, Azure Synapse, Census +7 | BigQuery, Census, Hightouch, Luca AI, Snowflake against: Airbyte, Amazon Redshift, Databricks +6 | Census, Hightouch Grouparoo, RudderStack against: Airbyte, Fivetran, Funnel +7 |
Normalization in this category
Every judgment call made between the raw labels and the numbers above, listed so it is visible and reversible.
Category-scoped readings
None. Every name in this category resolved on its own.
Unresolved, counted raw
Oracle
Discontinued, still offered
Perplexity Sonar Pro named Grouparoo as alternative on the negative prompt. Open source reverse ETL, shut down 2023.
Every prompt and answer
One row per call: the version string exactly as returned, whether the model searched, sources cited, latency and cost. Full answer text is in the responses download on the Data page.
| Prompt | Model | Version string | Time (UTC) | Searched | Sources | Latency | Cost |
|---|---|---|---|---|---|---|---|
| Direct recommendation | Claude Opus 5 | claude-opus-5 | 2026-09-08 14:23 | yes | 24 | 79 s | $0.24 |
| Direct recommendation | Claude Opus 4.8 | claude-opus-4-8 | 2026-09-08 14:23 | no | 0 | 28 s | $0.07 |
| Direct recommendation | GPT-6 Astra | gpt-6-astra | 2026-09-08 14:25 | yes | 7 | 51 s | $0.45 |
| Direct recommendation | GPT-5.6 Sol | gpt-5.6-sol | 2026-09-08 14:26 | yes | 7 | 61 s | $0.29 |
| Direct recommendation | Gemini 3.1 Pro | gemini-3.1-pro-preview | 2026-09-08 14:24 | no | 0 | 29 s | $0.03 |
| Direct recommendation | Perplexity Sonar Pro | sonar-pro | 2026-09-08 14:23 | yes | 20 | 14 s | $0.01 |
| Paraphrase | Claude Opus 5 | claude-opus-5 | 2026-09-08 14:27 | yes | 22 | 87 s | $0.25 |
| Paraphrase | Claude Opus 4.8 | claude-opus-4-8 | 2026-09-08 14:29 | yes | 29 | 79 s | $0.44 |
| Paraphrase | GPT-6 Astra | gpt-6-astra | 2026-09-08 14:30 | yes | 5 | 38 s | $0.29 |
| Paraphrase | GPT-5.6 Sol | gpt-5.6-sol | 2026-09-08 14:32 | yes | 5 | 74 s | $0.30 |
| Paraphrase | Gemini 3.1 Pro | gemini-3.1-pro-preview | 2026-09-08 14:30 | no | 0 | 28 s | $0.03 |
| Paraphrase | Perplexity Sonar Pro | sonar-pro | 2026-09-08 14:29 | yes | 19 | 7 s | $0.01 |
| Comparative | Claude Opus 5 | claude-opus-5 | 2026-09-08 14:33 | yes | 25 | 57 s | $0.24 |
| Comparative | Claude Opus 4.8 | claude-opus-4-8 | 2026-09-08 14:34 | yes | 35 | 71 s | $0.40 |
| Comparative | GPT-6 Astra | gpt-6-astra | 2026-09-08 14:36 | yes | 12 | 70 s | $0.51 |
| Comparative | GPT-5.6 Sol | gpt-5.6-sol | 2026-09-08 14:38 | yes | 10 | 73 s | $0.33 |
| Comparative | Gemini 3.1 Pro | gemini-3.1-pro-preview | 2026-09-08 14:35 | yes | 9 | 56 s | $0.06 |
| Comparative | Perplexity Sonar Pro | sonar-pro | 2026-09-08 14:34 | yes | 20 | 11 s | $0.02 |
| Budget constrained | Claude Opus 5 | claude-opus-5 | 2026-09-08 14:39 | yes | 40 | 85 s | $0.38 |
| Budget constrained | Claude Opus 4.8 | claude-opus-4-8 | 2026-09-08 14:40 | no | 0 | 22 s | $0.06 |
| Budget constrained | GPT-6 Astra | gpt-6-astra | 2026-09-08 14:42 | yes | 7 | 45 s | $0.35 |
| Budget constrained | GPT-5.6 Sol | gpt-5.6-sol | 2026-09-08 14:43 | yes | 7 | 57 s | $0.23 |
| Budget constrained | Gemini 3.1 Pro | gemini-3.1-pro-preview | 2026-09-08 14:41 | yes | 10 | 53 s | $0.07 |
| Budget constrained | Perplexity Sonar Pro | sonar-pro | 2026-09-08 14:40 | yes | 20 | 12 s | $0.02 |
| Scale constrained | Claude Opus 5 | claude-opus-5 | 2026-09-08 14:44 | yes | 24 | 79 s | $0.28 |
| Scale constrained | Claude Opus 4.8 | claude-opus-4-8 | 2026-09-08 14:45 | no | 0 | 33 s | $0.08 |
| Scale constrained | GPT-6 Astra | gpt-6-astra | 2026-09-08 14:47 | yes | 11 | 65 s | $0.47 |
| Scale constrained | GPT-5.6 Sol | gpt-5.6-sol | 2026-09-08 14:48 | yes | 9 | 76 s | $0.27 |
| Scale constrained | Gemini 3.1 Pro | gemini-3.1-pro-preview | 2026-09-08 14:46 | no | 0 | 40 s | $0.03 |
| Scale constrained | Perplexity Sonar Pro | sonar-pro | 2026-09-08 14:45 | yes | 20 | 14 s | $0.02 |
| Negative framing | Claude Opus 5 | claude-opus-5 | 2026-09-08 14:50 | yes | 35 | 91 s | $0.43 |
| Negative framing | Claude Opus 4.8 | claude-opus-4-8 | 2026-09-08 14:52 | yes | 32 | 155 s | $0.32 |
| Negative framing | GPT-6 Astra | gpt-6-astra | 2026-09-08 14:55 | yes | 6 | 49 s | $0.41 |
| Negative framing | GPT-5.6 Sol | gpt-5.6-sol | 2026-09-08 14:57 | yes | 10 | 82 s | $0.41 |
| Negative framing | Gemini 3.1 Pro | gemini-3.1-pro-preview | 2026-09-08 14:54 | yes | 8 | 101 s | $0.06 |
| Negative framing | Perplexity Sonar Pro | sonar-pro | 2026-09-08 14:53 | yes | 20 | 13 s | $0.02 |