Full-Stack Toolbox 🧰: Python🐍 Edition

TL;DR

Here are Python libraries that focus on different aspects of full-stack development; some focus on web application development, some on the back end, and others on both.

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1. Taipy

Taipy is an open-source Python library for building production-ready applications front-end & back-end.
It has been designed to expedite application development, from initial prototypes to production-ready applications.

🔑 Features:

• Extensive interactivity
• There are more customization capabilities for your layout, styling, etc. (no CSS needed)
• Multipage & multi-user applications
• Graphical Pipeline Editor
• Integration with Taipy Front-end capabilities for an end-to-end deployment
• Scheduling
• Notebook compatible

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2.Prefect

Prefect is a framework offering data pipeline development.
It sets itself apart from competitors like Airflow by focusing on simplicity and user-friendliness.
Prefect is a good in-between if you want a mature product with various features but an easier learning curve than Airflow.

🔑 Features:

• Intuitive and user-friendly control panel
• intelligent features such as caching
• Flow-based structure to ease into workflow organization
• Dynamic parametrization & dependency management
• Hybrid execution ( Local/Cloud)

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3. Streamlit

Streamlit is a game changer for quickly and simply building web applications.
This data app framework focused their differentiation from more complete and complex solutions to a quick and user-friendly solution.

🔑 Features:

• Pythonic framework
• Interactive widgets
• User-friendliness

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4.Airflow

Apache Airflow is an open-source platform designed for scheduling and monitoring workflows.
It's an excellent choice for orchestrating complex data pipelines and ETL processes, as it has been a prominent player in the pipeline landscape for the last decade.
The completeness of this library is associated with a steep learning curve.

🔑 Features:

• DAG-Based Workflow Definition
• The complete interface that includes visualization of DAGs tracks failures and manages retries.
• Various integration
• Dynamic Task Execution and Scheduling
• Python-Centric Design
• Community Support

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5. Brython

Brython brings Python to the front-end, as the name regroups “Browser” and “Python”.
It introduces a unique concept of running Python code directly in web applications.
This innovative approach allows you to build interactive web experiences using Python.

🔑 Features:

• Easy integration as it integrates the Python logic directly into the browser
• Compatible with different web browsers

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6. Dash

Dash, created by Plotly, is a web application framework using the completeness of Plotlys’ components.
It is known to be a complete solution, and with that comes a longer time to master.

🔑 Features:

• Component-based architecture
• Powerful interactive dashboards
• Real-time data updates

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7. Dagster

Dagster, one of the newer libraries in this compilation, is a cloud-native data pipeline orchestration aiming to unify data integration, workflow orchestration, and monitoring.

Compared to other tools, Dagster emphasizes the DataOps aspect of the workflow creation and management.

🔑 Features:

• Declarative pipeline setup
• Opinionated structure
• Versioning
• Integration with Hadoop
• Comprehensive metadata tracking

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8. SQLAlchemy

SQLAlchemy is a Python library used to handle database interaction.
It is a versatile toolkit that links Python programming and relational databases.
The time to master this library is longer, but the benefits are real.

🔑 Features:

• SQL Expression language
• Object-Relation Mapping (ORM)
• Wide support for databases
• Complex query support

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9. Celery

Celery is a Python library used as a framework to build back-end applications.
It handles distributed task processing, job executions, and workflows.
It is a complete solution and extensive feature set with a steeper learning curve.

🔑 Features:

• Parallel processing
• Scalability
• Task scheduling
• Asynchronous processing

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10. Peewee

Like SQAlchemy, Peewee is a Python library that simplifies database interaction.
It differs from its competitors with a user-friendly and simple setup.
This library is the best option for small to medium projects.

🔑 Features:

• Lightweight framework
• Database support
• Pythonic syntax

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11. Kedro

Kedro is an open-source Python framework.
It provides a toolbox for production-ready data science pipelines.
Indeed, Kedro easily integrates with well-established Python ML libraries and provides a unified way to implement an end-to-end framework.

🔑 Features:

• Data Catalog
• Notebooks integration
• Project template
• Opinionated as it enforces specific conventions

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Conclusion

Python is considered the go-to language for AI and Machine learning applications.
To streamline and automate these applications, Python has been witnessing the emergence of framework libraries.
These libraries facilitate web application development, back-end services, and the creation of open-source full-stack applications.

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