Table of Contents
This tutorial provides a comprehensive guide on leveraging the functionalities of Pydantic and SQLAlchemy within Python applications. Pydantic is utilized primarily for data validation and settings management through data parsing and validation using Python type annotations. SQLAlchemy, on the other hand, serves as a powerful SQL toolkit and Object-Relational Mapping (ORM) system for Python, facilitating database communication more efficiently and intuitively.
The aim of this tutorial is to showcase how these two libraries can be integrated to build robust data processing applications. You'll learn how to validate data effectively with Pydantic and perform database operations seamlessly with SQLAlchemy. Whether you're managing data flows for web development with FastAPI or orchestrating complex data transformations for ETL processes, this guide will provide you with the necessary tools and knowledge to enhance your projects' architecture and data integrity.
This guide is ideal for developers looking to improve their backend systems, data scientists seeking to streamline their data handling, and anyone in between who wishes to master these powerful Python libraries.
-
Clone the repository
git clone https://github.com/jpcadena/pydantic-sqlalchemy-tutorial.git
-
Change the directory to root project
cd pydantic-sqlalchemy-tutorial
-
Install Poetry package manager
pip install poetry
-
Install the project's dependencies
poetry install
-
Activate the environment
poetry shell
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Setting up environment variables:
If you find a
.env.sample
in the project directory, make a copy of it and rename to.env
.cp .env.sample .env
This
.env
file will be used to manage your application's environment variables. -
Configuring your credentials:
Open the
.env
file in a text editor and replace the placeholder values with your actual credentials.# .env file POSTGRES_USER=your_database_user SECRET_KEY=your_api_key
-
Execute with console
python main.py
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