Prompt
I need a warehouse that works with dbt, Looker, and Airflow—what should I use?
Latest observation
If you want a warehouse that plays well with dbt, Looker, and Airflow, the safest default choice is usually:
Best overall: Snowflake
Why it’s a strong fit:
- dbt: Excellent support and very common in dbt-native stacks
- Looker: Strong compatibility and widely used together
- Airflow: Easy to orchestrate via providers/operators
- General benefits: Scales well, separates compute/storage, and is simple for analytics teams to operate
Good alternatives
BigQuery
Best if you:
- already live in GCP
- want very low ops overhead
- like serverless analytics
It also works very well with dbt, Looker, and Airflow.
Redshift
Best if you:
- are deep in AWS
- want tighter AWS integration
- already have infra around Redshift
It works, but in many modern analytics stacks Snowflake/BigQuery are more common.
Quick recommendation by cloud
- AWS-heavy org: Snowflake or Redshift
- GCP-heavy org: BigQuery
- Want easiest cross-tool compatibility and broad adoption: Snowflake
My practical advice
If you don’t have a strong cloud preference already, pick Snowflake.
If you want, I can also give you:
- a decision matrix comparing Snowflake vs BigQuery vs Redshift for your stack, or
- a recommended architecture for dbt + Looker + Airflow.
Observation history
How did Obsurfable measure this prompt?
Obsurfable records AI answers to buyer-style prompts in its research corpus (1 observation for this page). Metrics are distributions over observations, not a single static ranking.
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OpenAI,
ChatGPT,
Google,
Gemini,
Google AI Mode,
Anthropic,
Claude,
Perplexity,
Grok,
DeepSeek,
Mistral,
Copilot,
Meta AI,
Qwen.
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