BigQuery vs dbt
Seven of fourteen models named BigQuery first on the direct prompt; zero named dbt. Both were named by all fourteen models and BigQuery carries 75 labels and dbt 50, so the shares are not directly comparable.
By Google, Mountain View, California, United States, founded 1998. Named in five categories this edition.
By dbt-core. Named in four categories this edition.
Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all fourteen models, for a mid-market B2B company; rank is within the category; every quote names the model and the prompt it came from. Both figures come from the Data warehouse and reverse ETL for marketing page.
By framing
BigQueryFirst choices, of fourteen modelsdbt
Across every category in the November 2026 Edition, BigQuery and dbt were named in the same answer 112 times, of the 245 answers naming BigQuery and the 142 naming dbt. In those answers dbt took the first choice one time and BigQuery sixty-five.
Every model, every framing
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | | | | | ||
| GPT-5.4 mini | | | | | | |
| Gemini 3.5 Flash | | | | | | |
| Perplexity Sonar | | | | | ||
| Grok 4.20 | | | | | | |
| Mistral Small | | | | | | |
| DeepSeek V4 Flash | | | | | | |
| Llama 4 Maverick | | | | |||
| Qwen 3.7 Flash | | | | | | |
| Kimi K2 | | | | | | |
| GLM 4.7 FlashX | | | | | | |
| MiniMax M2.5 | | | | | | |
| GPT-6 Luna | | | | | | |
| Muse Glimmer 30B | | | | |
BigQuery dbt first choice named as an alternative argued againstblank: not namedEach cell is one answer, BigQuery on the left and dbt on the right.The direct prompt
BigQuery first, dbt an alternative
7 of 14 modelsdbt was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
7 of 14 modelsThe answer put something else first and named one of the two as an alternative.Bold names in an answer are the products the judge labeled a first choice; a model naming several gives each of them that label. The full answer text for every row is in the record.
By buyer segment
BigQuery44%#1 of 18dbt0%#6 of 18What the models said about BigQuery
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of five in this category shown.
“Using Snowflake, BigQuery, or Databricks directly for marketing activation... they offer zero native activation” Qwen 3.7 Flash · negative prompt · soft negative
“marketing teams should be careful if they need tight cost control and predictable execution at scale” GPT-5.4 mini · negative prompt · soft negative
“If your budget is lean and you rely heavily on GA4/Google Ads: Go with Google BigQuery (DW) + Hightouch or Census (Reverse ETL).” Gemini 3.5 Flash · comparative prompt · first choice
“Choose BigQuery if you're heavily invested in Google Marketing Platform (GA4, Google Ads) — the native exports make setup trivial” Kimi K2 · direct prompt · first choice
“Often the #1 choice for marketing teams because it natively connects to Google's marketing platforms without third-party ETL tools.” DeepSeek V4 Flash · comparative prompt · first choice
What the models said about dbt
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of four in this category shown.
“Tools like dbt (for transformations) add value but increase complexity.” Grok 4.20 · negative prompt · soft negative
“Transformation | dbt Core (open-source) | The industry standard for modeling marketing data” Grok 4.20 · budget prompt · first choice
“Build reusable, tested SQL models for customers, campaigns, spend, and attribution without starting with a paid platform.” GPT-6 Luna · budget prompt · alternative
“Alternatively, dbt (data build tool) is excellent for transforming data within your warehouse and is open-source” Mistral Small · budget prompt · alternative
Comparisons are drawn for the top eight products in each category, each against each. The output is the models' output; nothing here is a recommendation by the index.