Prompt
Do I need a warehouse if I use dbt and a BI tool already?
Latest observation
Usually, yes.
A BI tool and dbt do different jobs, and neither replaces a data warehouse.
What each piece does
- Data warehouse: stores your raw and modeled data centrally, and runs queries at scale.
- dbt: transforms data inside the warehouse using SQL and manages your data models/tests/docs.
- BI tool: visualizes and explores data for reporting and dashboards.
Why you still need a warehouse
dbt works by connecting to a warehouse like:
- Snowflake
- BigQuery
- Redshift
- Databricks SQL
- Postgres, etc.
It doesn’t store data itself, and a BI tool typically only reads from a storage/query layer. Without a warehouse, you’d need some other system that can:
- store the data,
- handle analytics queries,
- serve results to dbt and the BI tool.
When you might not need a traditional warehouse
You might not need a separate “warehouse product” if:
- your BI tool connects directly to a source database, and
- dbt runs transformations in that same database.
But that database is still acting as your warehouse for analytics purposes.
Simple rule of thumb
If you want:
- reliable analytics,
- centralized modeled data,
- SQL transformations with dbt,
- dashboards in a BI tool,
then you need a data warehouse or warehouse-like analytical database.
If you want, I can also sketch a typical stack architecture showing how dbt + warehouse + BI fit together.
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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