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dbt (data build tool) transforms data in your warehouse using SQL SELECT statements. Learn project setup, models, tests, documentation, incremental materializations, and integration with data warehouses like PostgreSQL, BigQuery, and Snowflake.
Passed 21 of 25 test cases with the skill, and 19 of 25 without it.
2 fixed19 passed either way4 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 2 test cases the skill fixed. Same model, same prompt, with and without the skill.
Create a dbt staging SQL model file for stg_users that cleans user records coming from source('raw', 'users'). Ensure emails are converted to lowercase and trimmed of whitespace, creation timestamps are cast to timestamp, and invalid rows without emails are removed.
| Checked | Without skill | With skill |
|---|---|---|
| Transforms the email field using LOWER(TRIM(email)). | Passed | Passed |
| Casts created_at using created_at::timestamp. | Missed | Passed |
| Filters rows using WHERE email IS NOT NULL. | Passed | Passed |
The instructions your model reads when the skill loads.
dbt lets analytics engineers transform data by writing SQL SELECT statements. It handles materialization (tables, views, incremental), testing, documentation, and lineage tracking.
# Install dbt with PostgreSQL adapter
pip install dbt-postgres
# Or with other adapters
pip install dbt-bigquery
pip install dbt-snowflake
# Initialize a new project
dbt init my_project
cd my_project
my_project/
├── dbt_project.yml # Project configuration
├── profiles.yml # Connection profiles (usually in ~/.dbt/)
├── models/
│ ├── staging/ # Raw data cleaning
│ │ ├── _staging.yml # Schema + tests for staging models
│ │ ├── stg_users.sql
│ │ └── stg_orders.sql
│ └── marts/ # Business logic
│ ├── _marts.yml
│ └── fct_revenue.sql
├── tests/ # Custom data tests
├── macros/ # Reusable SQL macros
└── seeds/ # CSV files to load
test cases passed, vs 19 / 25 without
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