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Laravel VS Jupyter

Compare Laravel VS Jupyter and see what are their differences

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Laravel logo Laravel

A PHP Framework For Web Artisans

Jupyter logo Jupyter

Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. Ready to get started? Try it in your browser Install the Notebook.
  • Laravel Landing page
    Landing page //
    2023-07-24
  • Jupyter Landing page
    Landing page //
    2023-06-22

Laravel features and specs

  • Eloquent ORM
    Laravel includes Eloquent ORM, which provides a beautiful and simple ActiveRecord implementation for working with your database. It allows for easy interaction with your databases, offering an intuitive syntax.
  • Blade Templating Engine
    The Blade templating engine offers a clean and efficient syntax for writing templates. It provides features like template inheritance and sections, which makes template design more manageable and organized.
  • Artisan CLI
    Laravel's Artisan Command Line Interface (CLI) allows developers to perform repetitive tasks and manage their Laravel project more efficiently with built-in commands for database migration, seeding, and building tasks.
  • Strong Community and Ecosystem
    Laravel has a large and active community that provides an abundance of resources, including packages, tutorials, and screencasts on Laracasts. This ecosystem allows for quick problem-solving and an extensive library of reusable components.
  • Robust Security Features
    Laravel provides built-in security features such as salted and hashed passwords, encryption, and protection against common vulnerabilities like SQL injection and cross-site request forgery (CSRF).
  • Efficient Testing
    Laravel comes with PHPUnit integrated, along with convenient helper methods, making writing test cases and performing automated testing more straightforward. This leads to better code reliability and fewer bugs.
  • Comprehensive Documentation
    Laravel has thorough and well-organized documentation that covers all its features in detail. This makes it easier for new and experienced developers to understand and use the framework effectively.

Possible disadvantages of Laravel

  • Performance Overhead
    Since Laravel is a full-featured framework, it includes many built-in functions and layers that can create performance overhead. For very high-performance applications, fine-tuning may be necessary.
  • Steep Learning Curve for Beginners
    For those new to web development or coming from a different programming paradigm, Laravel can be challenging to grasp initially due to its extensive features and modern PHP practices.
  • Heavy Dependency on Composer
    Laravel heavily relies on Composer for dependency management. While this is beneficial for package management, it can be a downside if you are not familiar with Composer or have issues managing packages.
  • Frequent Updates
    Laravel receives frequent updates and changes in the new versions, which can sometimes lead to compatibility issues with existing projects. Keeping up with the updates can be time-consuming.
  • Hosting Requirements
    Laravel requires specific server configurations and dependencies, which may not be available on all shared hosting services. This can necessitate using a Virtual Private Server (VPS) or dedicated server, which might have higher costs.

Jupyter features and specs

  • Interactive Computing
    Jupyter allows real-time interaction with the data and code, providing immediate feedback and making it easier to experiment and iterate.
  • Rich Media Output
    It supports output in various formats including HTML, images, videos, LaTeX, and more, enhancing the ability to visualize and interpret results.
  • Language Agnostic
    Jupyter supports multiple programming languages through its kernel system (e.g., Python, R, Julia), allowing flexibility in the choice of tools.
  • Collaborative Features
    It enables collaboration through shared notebooks, version control, and platform integrations like GitHub.
  • Educational Tool
    Jupyter is widely used for teaching, thanks to its easy-to-use interface and ability to combine narrative text with code, making it ideal for assignments and tutorials.
  • Extensibility
    Jupyter is highly extensible with a large ecosystem of plugins and extensions available for various functionalities.

Possible disadvantages of Jupyter

  • Performance Issues
    For larger datasets and more complex computations, Jupyter can be slower compared to running scripts directly in a dedicated IDE.
  • Version Control Challenges
    Managing version control for Jupyter notebooks can be cumbersome, as they are not plain text files and include metadata that can make diffing and merging complex.
  • Resource Intensive
    Running Jupyter notebooks can be resource-intensive, especially when working with multiple large notebooks simultaneously.
  • Security Concerns
    Because Jupyter allows code execution in the browser, it can be a potential security risk if notebooks from untrusted sources are run without restrictions.
  • Dependency Management
    Managing dependencies and ensuring that the notebook runs consistently across different environments can be challenging.
  • Less Suitable for Production
    Jupyter is often considered more as a research and educational tool rather than a production environment; transitioning from a notebook to production code can require significant refactoring.

Laravel videos

Laravel in 100 seconds

More videos:

  • Review - Why Laravel is Still Best in 2018

Jupyter videos

What is Jupyter Notebook?

More videos:

  • Tutorial - Jupyter Notebook Tutorial: Introduction, Setup, and Walkthrough
  • Review - JupyterLab: The Next Generation Jupyter Web Interface

Category Popularity

0-100% (relative to Laravel and Jupyter)
Developer Tools
100 100%
0% 0
Data Science And Machine Learning
Web Frameworks
100 100%
0% 0
Data Dashboard
0 0%
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Laravel and Jupyter

Laravel Reviews

Laravel vs. Symfony: A Comprehensive Comparison of PHP Frameworks
Laravel has a vibrant ecosystem with many first-party packages, such as Laravel Horizon for queue management, Laravel Echo for real-time events, and Laravel Sanctum for API authentication, that make it easy to extend functionality without much hassle.
The 20 Best Laravel Alternatives for Web Development
Oh, you bet. When devs talk Symfony, they’re eyeing robustness and a modular vibe that Laravel fans might miss. Its reusable components could tempt even the most loyal Laravel artisans to at least take a peek.
Top 9 best Frameworks for web development
The best frameworks for web development include React, Angular, Vue.js, Django, Spring, Laravel, Ruby on Rails, Flask and Express.js. Each of these frameworks has its own advantages and distinctive features, so it is important to choose the framework that best suits the needs of your project.
Source: www.kiwop.com
Top 5 Laravel Alternatives
However, there are other excellent choices other than Laravel as well. So, let’s check out some excellent Laravel alternatives before you hire Laravel developers India for your web development project. This post provides you with a thorough understanding of the available web development framework choices and their benefits over Laravel. For that, let’s first discuss the...
Framework review: Laravel vs CodeIgniter
Let's start with CodeIgniter first. It focuses on performance and speed. It offers a simple, easy-to-learn syntax, making it ideal for beginners. CodeIgniter uses its own proprietary Active Record implementation for database operations, which provides a simple and intuitive way to interact with data. Unlike Laravel, CodeIgniter does not enforce a specific architectural...
Source: infinyhost.com

Jupyter Reviews

Jupyter Notebook & 10 Alternatives: Data Notebook Review [2023]
Once you install nteract, you can open your notebook without having to launch the Jupyter Notebook or visit the Jupyter Lab. The nteract environment is similar to Jupyter Notebook but with more control and the possibility of extension via libraries like Papermill (notebook parameterization), Scrapbook (saving your notebook’s data and photos), and Bookstore (versioning).
Source: lakefs.io
7 best Colab alternatives in 2023
JupyterLab is the next-generation user interface for Project Jupyter. Like Colab, it's an interactive development environment for working with notebooks, code, and data. However, JupyterLab offers more flexibility as it can be self-hosted, enabling users to use their own hardware resources. It also supports extensions for integrating other services, making it a highly...
Source: deepnote.com
12 Best Jupyter Notebook Alternatives [2023] – Features, pros & cons, pricing
Jupyter Notebook is a widely popular tool for data scientists to work on data science projects. This article reviews the top 12 alternatives to Jupyter Notebook that offer additional features and capabilities.
Source: noteable.io
15 data science tools to consider using in 2021
Jupyter Notebook's roots are in the programming language Python -- it originally was part of the IPython interactive toolkit open source project before being split off in 2014. The loose combination of Julia, Python and R gave Jupyter its name; along with supporting those three languages, Jupyter has modular kernels for dozens of others.
Top 4 Python and Data Science IDEs for 2021 and Beyond
Yep — it’s the most popular IDE among data scientists. Jupyter Notebooks made interactivity a thing, and Jupyter Lab took the user experience to the next level. It’s a minimalistic IDE that does the essentials out of the box and provides options and hacks for more advanced use.

Social recommendations and mentions

Laravel might be a bit more popular than Jupyter. We know about 240 links to it since March 2021 and only 216 links to Jupyter. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Laravel mentions (240)

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Jupyter mentions (216)

  • The 3 Best Python Frameworks To Build UIs for AI Apps
    Showcase and share: Easily embed UIs in Jupyter Notebook, Google Colab or share them on Hugging Face using a public link. - Source: dev.to / about 2 months ago
  • LangChain: From Chains to Threads
    LangChain wasn’t designed in isolation — it was built in the data pipeline world, where every data engineer’s tool of choice was Jupyter Notebooks. Jupyter was an innovative tool, making pipeline programming easy to experiment with, iterate on, and debug. It was a perfect fit for machine learning workflows, where you preprocess data, train models, analyze outputs, and fine-tune parameters — all in a structured,... - Source: dev.to / 3 months ago
  • Applied Artificial Intelligence & its role in an AGI World
    Leverage versatile resources to prototype and refine your ideas, such as Jupyter Notebooks for rapid iterations, Google Colabs for cloud-based experimentation, OpenAI’s API Playground for testing and fine-tuning prompts, and Anthropic's Prompt Engineering Library for inspiration and guidance on advanced prompting techniques. For frontend experimentation, tools like v0 are invaluable, providing a seamless way to... - Source: dev.to / 4 months ago
  • Jupyter Notebook for Java
    Lately I've been working on Langgraph4J which is a Java implementation of the more famous Langgraph.js which is a Javascript library used to create agent and multi-agent workflows by Langchain. Interesting note is that [Langchain.js] uses Javascript Jupyter notebooks powered by a DENO Jupiter Kernel to implement and document How-Tos. So, I faced a dilemma on how to use (or possibly simulate) the same approach in... - Source: dev.to / 8 months ago
  • JIRA Analytics with Pandas
    One of the most convenient ways to play with datasets is to utilize Jupyter. If you are not familiar with this tool, do not worry. I will show how to use it to solve our problem. For local experiments, I like to use DataSpell by JetBrains, but there are services available online and for free. One of the most well-known services among data scientists is Kaggle. However, their notebooks don't allow you to make... - Source: dev.to / 11 months ago
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What are some alternatives?

When comparing Laravel and Jupyter, you can also consider the following products

Django - The Web framework for perfectionists with deadlines

Looker - Looker makes it easy for analysts to create and curate custom data experiences—so everyone in the business can explore the data that matters to them, in the context that makes it truly meaningful.

Ruby on Rails - Ruby on Rails is an open source full-stack web application framework for the Ruby programming...

Databricks - Databricks provides a Unified Analytics Platform that accelerates innovation by unifying data science, engineering and business.‎What is Apache Spark?

CodeIgniter - A Fully Baked PHP Framework

Google BigQuery - A fully managed data warehouse for large-scale data analytics.