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IntelliJ IDEA VS Jupyter

Compare IntelliJ IDEA VS Jupyter and see what are their differences

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IntelliJ IDEA logo IntelliJ IDEA

Capable and Ergonomic IDE for JVM

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.
  • IntelliJ IDEA Landing page
    Landing page //
    2023-07-20
  • Jupyter Landing page
    Landing page //
    2023-06-22

IntelliJ IDEA features and specs

  • Intelligent Code Completion
    IntelliJ IDEA offers advanced code completion features, which help to speed up coding by suggesting relevant keywords, variable names, and methods. It understands the context of the code, making suggestions more accurate and useful.
  • Powerful Refactoring
    IntelliJ IDEA provides an extensive set of refactoring tools that simplify code restructuring. Developers can easily rename, move, and change the signature of classes and methods without breaking the application.
  • Integrated Version Control
    Supports various version control systems such as Git, SVN, and Mercurial. This integration allows seamless code commits, merges, and history tracking directly within the IDE.
  • Rich Plugin Ecosystem
    Offers a vast library of plugins to extend its functionalities by adding support for additional languages, frameworks, and tools, making it highly customizable.
  • Built-in Terminal
    Includes a powerful built-in terminal that allows for command-line operations without leaving the IDE, improving workflow efficiency.
  • Cross-Platform Support
    IntelliJ IDEA is available for Windows, macOS, and Linux, providing consistency for developers who work across different operating systems.
  • Comprehensive Debugging
    Features advanced debugging tools that provide detailed inspection of variable states, stack traces, and execution flow, making it easier to identify and fix issues.
  • User-Friendly Interface
    Offers a highly intuitive and customizable user interface that simplifies navigation and enhances productivity.

Possible disadvantages of IntelliJ IDEA

  • Resource Intensive
    IntelliJ IDEA can be very demanding on system resources, such as RAM and CPU, which may affect performance on less powerful machines.
  • Steep Learning Curve
    New users may find it challenging to learn all of its features and settings, as the IDE offers a wide range of tools and options.
  • Cost
    While there is a free Community edition, the Ultimate edition with full features requires a paid license, which can be expensive for individual developers or small teams.
  • Occasional Sluggishness
    Users may experience occasional sluggish performance, especially with large projects or extensive usage of plugins.
  • Initial Setup Complexity
    Setting up IntelliJ IDEA for the first time, particularly for specific project configurations, can be time-consuming and complex.
  • Frequent Updates
    While regular updates are beneficial for security and new features, they can sometimes cause interruptions or require downtime to install.
  • Dependency on Plugins
    Some key functionalities may rely heavily on third-party plugins, which can sometimes lag in updates or lack sufficient documentation.

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.

IntelliJ IDEA videos

+ Add

Overview of IntelliJ IDEA

More videos:

  • Review - Why I Use IntelliJ IDEA
  • Review - Be More Productive With IntelliJ IDEA by Trisha Gee

Jupyter videos

+ Add

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 IntelliJ IDEA and Jupyter)

IntelliJ IDEA

Jupyter

IDE
100 100%
IDE
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Text Editors
100 100%
Text Editors
0% 0
Data Dashboard
0 0%
Data Dashboard
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 IntelliJ IDEA and Jupyter

IntelliJ IDEA Reviews

Explore 9 Top Eclipse Alternatives for 2024
Developed by JetBrains, IntelliJ IDEA is a versatile Integrated Development Environment (IDE) initially built for Java and Kotlin, but extends its prowess to incorporate a myriad of other languages through plugins. A unique and user-centric environment favorably touted for its tuneability and keyboard shortcuts that enhance productivity, IntelliJ IDEA has grown to compete...
Source: aircada.com
Top 10 Android Studio Alternatives For App Development
IntelliJ IDEA is an IDE that was developed by JetBrains and is written in Java, Groovy, and Kotlin. It helps in building the IntelliJ IDEA community from the source code and also produces high-quality code.
Source: www.geeksforgeeks.org
The Best IDEs for Java Development: A Comparative Analysis
Intelligent Coding Assistance: What sets IntelliJ IDEA apart is its intelligent coding assistance. It seems to understand your code, predict your needs, and assist you with a range of development tasks from start to finish. By virtually mapping your Java projects, it can detect errors, suggest code variants, conduct refactoring, and more. It’s like having a coding assistant!
Source: dev.to
20 Best Diff Tools to Compare File Contents on Linux
Intellij Idea is an enterprise development, cross-platform software for multiple operating systems. It is used to review the differences between any two files, folders, text sources, or database objects, as well as between local files and their repository versions.
Source: linuxopsys.com
9 Of The Best Android Studio Alternatives To Try Out
IntelliJ IDEA, the routine tasks like coding, are handled by IntelliJ IDEA, which leads to accelerated development. It also allows the programmer to focus on functionality.
Source: technicalustad.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.
Source: searchbusinessanalytics.techtarget.com
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.
Source: towardsdatascience.com

Social recommendations and mentions

Based on our record, Jupyter seems to be more popular. It has been mentiond 216 times since March 2021. 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.

IntelliJ IDEA mentions (0)

We have not tracked any mentions of IntelliJ IDEA yet. Tracking of IntelliJ IDEA recommendations started around Mar 2021.

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 1 month 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 IntelliJ IDEA and Jupyter, you can also consider the following products

Microsoft Visual Studio - Microsoft Visual Studio is an integrated development environment (IDE) from Microsoft.

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.

VS Code - Build and debug modern web and cloud applications, by Microsoft

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

Xcode - Xcode is Apple’s powerful integrated development environment for creating great apps for Mac, iPhone, and iPad. Xcode 4 includes the Xcode IDE, instruments, iOS Simulator, and the latest Mac OS X and iOS SDKs.

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

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