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BigQuery dbt model scaffolder from YAML data contracts. Generates Data Products with auto introspection.

Project description

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👻 wraith-modelgen

Sovereign dbt Scaffolding for the Ghost Stack

wraith-modelgen is a BigQuery dbt model scaffolder. Feed it a YAML data contract, get back a pair of sovereign data products: an Origin (raw) layer that passes everything through, and a Consumption (staging) layer that holds the stable line for downstream consumers.

Source schemas change. Columns get renamed. Types get tightened. New fields appear without ceremony. In most analytics codebases this is a downstream catastrophe: dashboards break, FDPs fail, the analytics team gets paged at 03:00, and someone files a ticket asking whether dbt itself is broken.

wraith-modelgen makes the data contract the change-management mechanism. The source team updates it as part of their release. wraith-modelgen regenerates the models. Downstream sees what it sees. Nothing breaks unless something it actually depended on disappears, which triggers a generation failure with the column name.


🚀 Installation

uv tool install wraith-modelgen

You'll also need Application Default Credentials for BigQuery introspection:

gcloud auth application-default login

⚙️ Workflow

modelgen init                                  # scaffold .modelgen.yml + dirs (once)
modelgen new my-project.landing.user_signups   # draft a contract from a live table
modelgen validate contracts/user_signups.yml   # offline semantic lint, all findings at once
modelgen run                                   # regenerate every layer of every contract
modelgen check --diff                          # CI: fail if committed models drifted

Single layers when you need them — --raw and --staging are mutually exclusive:

modelgen gen contract.yml --raw     -o ./models/raw
modelgen gen contract.yml --staging -o ./models/staging
modelgen gen contract.yml --raw --dry-run  # preview without writing
Command What it does
init Scaffold project layout: .modelgen.yml, contracts/, models/
new Bootstrap a draft contract from a live BigQuery table, keys guessed and marked TODO
validate Offline structural + semantic lint; --strict promotes warnings to failures
gen Generate one layer from one contract
run Regenerate every layer of every contract under .modelgen.yml
check Render in memory, diff against disk, exit non-zero on drift. Writes nothing.

📄 Contract anatomy

version: "1"

event:
  name: user_signed_up                 # the entity
  unique_key: EVENT_ID                 # SOURCE column name (composite: [a, b])
  loaded_at_field: RECEIVED_AT         # SOURCE column name

  source:
    project: my-gcp-project
    dataset: landing
    table: user_signups_raw

  raw:
    dataset: raw
    incremental_strategy: merge
    dedup: true                        # row_number() partition by unique_key
    partition_by:
      field: RECEIVED_AT
      data_type: timestamp
      granularity: day
    cluster_by: [USER_ID]

  staging:
    dataset: staging
    incremental_strategy: merge
    partition_by:
      field: received_at               # staging-side name (post-rename)
      data_type: timestamp
      granularity: day
    cluster_by: [user_id]

    columns:
      - source: EVENT_ID               # name in raw (== name in source)
        name: event_id                 # name in staging
        type: STRING                   # cast target
        description: "..."
        tests: [not_null, unique]

🔄 Schema evolution

Source change What wraith-modelgen does What you do
Adds a column Does not appear until you regenerate (modelgen run). Invisible in staging until declared in the contract. Regenerate, then add to staging when consumers need it.
Renames a column Validation fails: column not found in source. Update the source: field on that column. Also unique_key / loaded_at_field if applicable.
Retypes a column Existing CAST in staging absorbs it (or fails loudly at query time if values are incompatible). Update type: if the canonical type should change too.
Drops a column staging uses Validation fails: column not found. Either restore upstream or remove from staging contract.

Validation runs as part of modelgen gen --staging. If it passes, the generated staging model still presents the same contract to downstream consumers.


🏗️ What gets generated

For --raw:

  • raw__event.sql: dbt incremental model with {{ source(...) }}, optional dedup window, partition and cluster config.
  • raw__event.yml: dbt sources entry plus model definition. Columns mirror the introspected source schema.

For --staging:

  • stg__event.sql: dbt incremental model with {{ ref('raw__event') }}. Casts and renames applied.
  • stg__event.yml: model definition with column tests from the contract.

🧪 Developer Quality Gate

# Clone and set up
git clone https://git.thomaspeoples.com/thomaspeoples/wraith-modelgen
cd wraith-modelgen
uv run poe setup        # syncs deps + installs pre-commit hooks

# The quality gate
uv run poe test         # pytest (interactive)
uv run poe test-ci      # pytest with coverage enforcement (≥80%)
uv run poe lint         # ruff check
uv run poe format       # ruff format

The test suite uses FakeIntrospector and runs without BigQuery credentials. Covers contract parsing, semantic linting, both layers, schema evolution scenarios, composite keys, REPEATED/RECORD types, determinism, drift detection, contract scaffolding, and error surfaces.

Committing

All commits go through commitizen with the Ghost Stack convention:

👻 <type>/<ticket>: <message>
uv run cz commit

📜 Sovereign Principles

  1. One layer per invocation. Layers have different lifecycles; conflating them makes things harder to reason about.
  2. Introspection at gen time. The warehouse is the source of truth for column names and types. Drift is impossible because nothing is duplicated.
  3. Deterministic output. Same contract + same source schema = byte-identical files. modelgen check turns this into a CI gate.
  4. Strict failure on missing columns. No silent passes. If the contract references a column the source no longer has, generation fails with the column name.
  5. BigQuery only. The introspection module is BQ-native. Add a different Introspector implementation if you need another warehouse.

Part of the Ghost Stack. Sovereign. Self-hosted. No nonsense.

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