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Typed, contract-driven data pipeline modeling for Python.

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Project description

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Pipelantic

Documentation Status

Typed, contract-driven data pipeline modeling for Python.

Define data, transformations, and pipelines with typed Python classes. Validate and plan them once. Execute them through interchangeable backends.

Status

0.2.0 — Contract Interoperability

Pipelantic provides the typed modeling kernel plus ODCS/DTCS/DPCS generate and load, deterministic contract bundles, and compatibility hooks. Planning and execution plugins arrive in later milestones.

See the hosted documentation for the full design, CHANGELOG.md for release notes, and Roadmap for sequencing.

Install

pip install pipelantic
# or
uv add pipelantic

Development

Requires uv.

uv sync
uv run pytest
uv run ruff check .
uv run ruff format .

uv sync creates .venv, installs the package in editable mode, and installs the dev dependency group (pytest, ruff, mkdocs) by default.

Release

Tag a version that matches src/pipelantic/_version.py, then push the tag:

git tag v0.2.0
git push origin v0.2.0

GitHub Actions runs checks and publishes to PyPI using the PYPI_API_TOKEN repository secret.

Quick example

from contractmodel import ContractModel as DataContractModel
from pipelantic import Input, Output, Pipeline, Sink, Source, Transformation


class RawCustomer(DataContractModel):
    customer_id: int
    first_name: str
    last_name: str


class Customer(DataContractModel):
    customer_id: int
    full_name: str


class NormalizeCustomers(Transformation):
    customers: Input[RawCustomer]
    result: Output[Customer]


class CustomerPipeline(Pipeline):
    raw: Source[RawCustomer] = Source(binding="customer_source")
    normalized = NormalizeCustomers.step(customers=raw)
    curated: Sink[Customer] = Sink(
        input=normalized.result,
        binding="customer_sink",
    )


graph = CustomerPipeline.inspect()
report = CustomerPipeline.validate()
print(CustomerPipeline.to_mermaid())

Documentation

License

MIT

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