Stop writing dbt boilerplate by hand — generate a complete staging layer, test YAML and mart skeleton from any schema input in seconds.
Paste a schema, CSV headers, sample JSON or an existing SQL query — get production-ready dbt staging models, schema YAML with tests and an optional mart layer, dialect-aware for BigQuery, Snowflake, Postgres and DuckDB.
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Models generated for orders
Generates stg_orders.sql, schema.yml with unique + not_null tests and an optional mart layer
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Staging CTAs, source() macros, renamed PKs and typed casts — the twenty minutes you skip every new table.
unique and not_null on primary keys, column descriptions in schema.yml — not something you add later.
DDL, CSV headers, a JSON API response or an existing SELECT — the generator handles all four starting points.
Staging model with {{ source() }} macro, renamed PKs, dialect-aware type casts and a clean CTE pattern
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Staging model with {{ source() }} macro, renamed PKs, dialect-aware type casts and a clean CTE pattern
schema.yml with source definition, column descriptions and not_null/unique tests wired to your PK
Optional fact or dimension mart model using {{ ref() }} pointing at the generated staging model
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In action
Staging CTAs, source() macros, renamed PKs and typed casts — the twenty minutes you skip every new table.
Try dbt ModelsModels generated for orders
Generates stg_orders.sql, schema.yml with unique + not_null tests and an optional mart layer
Go deeper
unique and not_null on primary keys, column descriptions in schema.yml — not something you add later.
Browse all premium toolsSalary, skills, employers and the Match Engine all read the same live posting feed — so you are not comparing a benchmark from 2022 against jobs from this week.