zendata 0.1.1

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Zendata

📦 Zendata Framework

A lightweight Python library that standardizes input and output data handling across data processing pipelines. It provides a clean structure for defining tasks and services using Pydantic to validate both input and output data types at runtime.

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✨ Features

• ✅ Enforces strict typing and validation using pydantic
• ⚙️ Separates business logic from data structure
• 🔁 Reusable services with runtime checks
• 🧪 Simple to test and extend

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📚 Why Use This?

When working with data pipelines, it's common to pass data between stages without validating it — leading to subtle bugs or type mismatches. This framework provides a standard way to wrap each step in a Task that holds both input and output, and a Service that processes data while validating contracts.

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🚀 Quick Start

1. Define a Task

from zendata.tasks.base import BaseTask, TaskStatus

task_definition = BaseTask(
    name="Task",
    input=int,
    output=int,
)

2. Create a Service

from zendata.tasks.service import Service, 
from zendata.tasks.task import Task, TaskStatus

class MyService(Service):
    def apply(self, task: Task) -> Task:
        # Your core logic
        task.output_data = task.input_data * 2
        return task

3. Run the Service

service = MyService(task_definition=task_definition, name="MultiplyByTwo")

result_task: Task = service.run(123)  # Will raise if types mismatch
print(result_task.output_data)

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📦 Install

uv sync

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🧪 Testing

make test