pipelantic 0.3.0

Typed, contract-driven data pipeline modeling for Python.

Pipelantic

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

0.3.0 — Validation and Pipeline Plan IR

Pipelantic provides the typed modeling kernel, contract interoperability, and an immutable secret-free PipelinePlan. Planning is pure (no secret resolution or execution). Local runtime arrives 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 v0.3.0
git push origin v0.3.0

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

Quick example

from pipelantic import Data, Input, Output, Pipeline, Sink, Source, Transformation


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


class Customer(Data):
    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

• Documentation site
• Getting Started
• Core Concepts
• Architecture
• Roadmap

License

MIT