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Python Inventory Data Pipeline (CLI + Modular Backend)

Status: Learning milestone completed – Python backend stage closed.

Python backend project focused on data processing and automation. Includes a modular architecture, a full CLI interface, JSON merging workflow, data validation, logging system, and reporting tools. Designed as a real backend-style pipeline for inventory updates and transformations.

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Overview

This repository groups my main Python backend work:

• A modular inventory system designed as a small data pipeline.
• A monolithic version of the same logic for comparison.
• A folder of Python exercises used to practice core language features and patterns.

The goal is to demonstrate the fundamentals of backend development with Python: clean code, data workflows, automation via CLI, and proper logging and error handling.

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Main Features

Inventory Data Pipeline

• Loads inventory and update files in JSON.
• Normalizes and restructures data.
• Applies updates (additions, changes, removals).
• Produces an updated inventory plus summary reports.

Command-Line Interface (CLI)

• Powered by argparse.
• Receives file paths and options from the terminal.
• Runs the entire workflow with a single command.

Modular Architecture

Separated into clear components:

• I/O operations (JSON load/save)
• Core inventory logic
• Reporting tools
• CLI entry point
• Logging configuration

Designed for maintainability and readability.

Logging System

• Centralized logging using Python’s logging module
• Informational and debug messages for key workflow steps
• Error logging for invalid files or unexpected data

Data Validation & Error Handling

• File existence checks
• JSON decoding protection
• Defensive handling of malformed or unknown entries

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Repository Structure

/inventario_modular/       # Modular implementation (recommended)
/inventario_monolitico/    # First monolithic prototype
/python_exercises/         # Practice exercises and learning scripts

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Tech Stack

Python 3.x Standard Library Only:

• argparse → CLI
• json → data handling
• logging → logging system
• pathlib → file paths
• Other built-ins where appropriate

No external dependencies required.

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How to Run the Modular Pipeline

Example invocation:

python -m inventario_modular \
    --inventario data/inventario.json \
    --actualizacion data/actualizacion.json \
    --salida salida/inventario_final.json \
    --top 10

Main CLI arguments:

• --inventario → path to the original inventory
• --actualizacion → path to the update file
• --salida → path to save the updated inventory
• --top → optional “Top N” report

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Learning Goals

This project was built to practice and demonstrate:

• Backend-oriented problem solving with Python
• Data processing and automation pipelines
• Building professional CLIs
• Clean, modular code structure
• Logging and error handling best practices

It showcases the transition from a basic script to a clean, extensible backend-style pipeline.

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