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Jupyter VS Apache Storm

Compare Jupyter VS Apache Storm and see what are their differences

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Contents:
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  3. » Reviews
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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.

Apache Storm logo Apache Storm

Apache Storm is a free and open source distributed realtime computation system.
  • Jupyter Landing page
    Landing page //
    2023-06-22
  • Apache Storm Landing page
    Landing page //
    2019-03-11

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.

Apache Storm features and specs

  • Real-Time Processing
    Apache Storm is designed for processing data in real-time, which makes it ideal for applications like fraud detection, recommendation systems, and monitoring tools.
  • Scalability
    Storm is capable of scaling horizontally, allowing it to handle increasing amounts of data by adding more nodes, making it suitable for large-scale applications.
  • Fault Tolerance
    Storm provides robust fault-tolerance mechanisms by rerouting tasks from failed nodes to operational ones, ensuring continuous processing.
  • Broad Language Support
    Apache Storm supports multiple programming languages, including Java, Python, and Ruby, allowing developers to use the language they are most comfortable with.
  • Open Source Community
    Being an Apache project, Storm benefits from a strong open-source community, which contributes to its development and offers abundant resources and support.

Possible disadvantages of Apache Storm

  • Complex Setup
    Setting up and configuring Apache Storm can be complex and time-consuming, requiring detailed knowledge of its architecture and the underlying infrastructure.
  • High Learning Curve
    The architecture and components of Storm can be difficult for new users to grasp, leading to a steeper learning curve compared to some other streaming platforms.
  • Maintenance Overhead
    Managing and maintaining a Storm cluster can require significant effort, including monitoring, troubleshooting, and scaling the infrastructure.
  • Error Handling
    While Storm is fault-tolerant, its error handling at the application level can sometimes be challenging, requiring careful design to manage failures effectively.
  • Resource Intensive
    Storm can be resource-intensive, particularly in terms of memory and CPU usage, which can lead to increased costs and necessitate powerful hardware.

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

Apache Storm videos

+ Add

Apache Storm Tutorial For Beginners | Apache Storm Training | Apache Storm Example | Edureka

More videos:

  • Review - Developing Java Streaming Applications with Apache Storm
  • Review - Atom Text Editor Option - Real-Time Analytics with Apache Storm

Category Popularity

0-100% (relative to Jupyter and Apache Storm)

Jupyter

Apache Storm

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Big Data
0 0%
Big Data
100% 100
Data Dashboard
92 92%
Data Dashboard
8% 8
Stream Processing
0 0%
Stream Processing
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 Jupyter and Apache Storm

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

Apache Storm Reviews

Top 15 Kafka Alternatives Popular In 2021
Apache Storm is a recognized, distributed, open-source real-time computational system. It is free, simple to use, and helps in easily and accurately processing multiple data streams in real-time. Because of its simplicity, it can be utilized with any programming language and that is one reason it is a developer’s preferred choice. It is fast, scalable, and integrates well...
Source: www.spec-india.com
5 Best-Performing Tools that Build Real-Time Data Pipeline
Apache Storm is an open-source distributed real-time computational system for processing data streams. Similar to what Hadoop does for batch processing, Apache Storm does for unbounded streams of data in a reliable manner. Built by Twitter, Apache Storm specifically aims at the transformation of data streams. Storm has many use cases like real-time analytics, online machine...
Source: www.analyticsinsight.net

Social recommendations and mentions

Based on our record, Jupyter seems to be a lot more popular than Apache Storm. While we know about 216 links to Jupyter, we've tracked only 11 mentions of Apache Storm. 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.

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
View more

Apache Storm mentions (11)

  • Data Engineering and DataOps: A Beginner's Guide to Building Data Solutions and Solving Real-World Challenges
    There are several frameworks available for batch processing, such as Hadoop, Apache Storm, and DataTorrent RTS. - Source: dev.to / over 2 years ago
  • Real Time Data Infra Stack
    Although this article lists a lot of targets for technical selection, there are definitely others that I haven't listed, which may be either outdated, less-used options such as Apache Storm or out of my radar from the beginning, like JAVA ecosystem. - Source: dev.to / over 2 years ago
  • In One Minute : Hadoop
    Storm, a system for real-time and stream processing. - Source: dev.to / over 2 years ago
  • Elon Musk reportedly wants to fire 75% of Twitter’s employees
    Google has scaled well and has helped others scale, Twitter has always been behind by years. I think the only thing they did well was Twitter Storm, now taken up by Apache Foundation. Source: over 2 years ago
  • Spark for beginners - and you
    Streaming: Sparks Streamings's latency is at least 500ms, since it operates on micro-batches of records, instead of processing one record at a time. Native streaming tools like Storm, Apex or Flink might be better for low-latency applications. - Source: dev.to / over 3 years ago
View more

What are some alternatives?

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

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.

Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.

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

Apache Flink - Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.

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

Presto DB - Distributed SQL Query Engine for Big Data (by Facebook)

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