BigQuery vs Census
Four of fourteen models named BigQuery first on the direct prompt; two named Census. Both were named by all fourteen models and BigQuery carries 73 labels and Census 80, so the shares are not directly comparable.
By Google, Mountain View, California, United States, founded 1998. Named in four categories this edition.
By Fivetran, United States. Named in five 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 modelsCensus
Across every category in the October 2026 Edition, BigQuery and Census were named in the same answer 184 times, of the 271 answers naming BigQuery and the 250 naming Census. In those answers Census took the first choice twenty-one times and BigQuery fifty-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 Census first choice named as an alternative argued againstblank: not namedEach cell is one answer, BigQuery on the left and Census on the right.The direct prompt
Both were the first choice
2 of 14 modelsThe answer named them together, and the judge labeled each a first choice.BigQuery first, Census an alternative
2 of 14 modelsCensus was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
10 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
BigQuery35%#2 of 17Census7%#3 of 17Census13%#3 of 13BigQuery6%#4 of 13What the models said about BigQuery
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of seven in this category shown.
“I wouldn't automatically avoid BigQuery, Snowflake, or Redshift... BigQuery charges for query processing... careless or frequent queries can affect the bill” GPT-6 Luna · negative prompt · soft negative
“"BigQuery is built for speed and scale, but marketing teams often discover its limitations only after months of custom pipeline work. Query cost spirals."” Muse Glimmer 30B · negative prompt · soft negative
“Query cost explosions: Exploratory queries on 1TB+ tables without partition filters can cost $50–$200 per run” DeepSeek V4 Flash · negative prompt · soft negative
“the most practical stack is usually BigQuery + self-hosted Airbyte + dbt + Metabase, with BigQuery as the warehouse” Perplexity Sonar · budget prompt · first choice
“BigQuery is often the default choice for modern marketing teams because of its deep integration with the Google ecosystem.” Gemini 3.5 Flash · comparative prompt · first choice
“Most marketing teams today pair BigQuery (for Google ecosystem) or Snowflake (for multi-cloud flexibility)” MiniMax M2.5 · comparative prompt · first choice
What the models said about Census
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“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 · hard negative
“Census assumes your data is already clean and modeled... You need a data engineering team or analytics engineer managing warehouse hygiene before Census delivers value.” Claude Haiku 4.5 · negative prompt · soft negative
“Pricing model shift: Moved from flat-fee to consumption-based Monthly Active Rows (MAR), making costs harder to predict” MiniMax M2.5 · negative prompt · soft negative
“compare Hightouch with Fivetran Activations (the current Fivetran product incorporating Census reverse-ETL capabilities)” GPT-6 Luna · scale prompt · first choice
“Both industry-leading Reverse ETL platforms offer a permanent free tier of 1 active destination with unlimited syncs/runs.” Gemini 3.5 Flash · budget prompt · first choice
“BigQuery + Fivetran + dbt + Census + Looker Studio is the strongest, most cost-effective combination” DeepSeek V4 Flash · direct prompt · first choice
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.