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Jupyter VS Composable Analytics

Compare Jupyter VS Composable Analytics and see what are their differences

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Turn SQL Data into Decisions. Build professional dashboards and data visualizations without technical expertise. Easily embed analytics anywhere, receive automated alerts, and discover AI-powered insights all through a straightforward interface. featured
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Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

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.

Composable Analytics logo Composable Analytics

Composable Analytics is an enterprise-grade analytics ecosystem built for business users that want to architect data intelligence solutions that leverage disparate data sources and event data.
  • Jupyter Landing page
    Landing page //
    2023-06-22
  • Composable Analytics Landing page
    Landing page //
    2022-04-06

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.

Composable Analytics features and specs

  • Flexibility
    Composable Analytics offers a flexible architecture that allows users to customize and build their analytics workflows according to specific needs, making it adaptable to a wide range of industries and use cases.
  • Integration Capabilities
    It supports integration with various data sources and tools, enabling seamless data flow and analysis across different platforms without requiring significant engineering resources.
  • User-Friendly Interface
    The platform provides a user-friendly interface that is designed to facilitate ease of use, even for non-technical users, empowering broader participation in data analytics tasks.
  • Scalability
    Composable Analytics is designed to be scalable, allowing businesses to handle growing amounts of data and increasing analytical demands as they expand.

Possible disadvantages of Composable Analytics

  • Complex Setup
    The initial setup and customization can be complex and time-consuming, requiring a clear understanding of the system's capabilities and integration points.
  • Learning Curve
    Despite its user-friendly interface, new users may experience a steep learning curve, especially those unfamiliar with data analytics or composable architectures.
  • Cost
    Depending on the scale and extent of use, the platform can be expensive, which might be a barrier for smaller businesses or startups with limited budgets.
  • Dependence on Third-Party Integrations
    Reliance on third-party tools and integrations might pose challenges if those external services are discontinued or change their API policies.

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

Composable Analytics videos

+ Add

World's #1st Data-Centric AIOps Platform | Composable Analytics for AIOps & Observability

Category Popularity

0-100% (relative to Jupyter and Composable Analytics)

Jupyter

Composable Analytics

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Development
0 0%
Development
100% 100
Data Dashboard
91 91%
Data Dashboard
9% 9
Business & Commerce
0 0%
Business & Commerce
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 Composable Analytics

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

Composable Analytics Reviews

We have no reviews of Composable Analytics yet.
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Social recommendations and mentions

Based on our record, Jupyter seems to be a lot more popular than Composable Analytics. While we know about 216 links to Jupyter, we've tracked only 1 mention of Composable Analytics. 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 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
View more

Composable Analytics mentions (1)

  • Ask HN: Who is hiring? (August 2021)
    - Front-End UI Developers passionate about creating well-architected user interfaces and fluent in current best practices for responsive and accessible design. - Junior and Senior level Software Engineers that have the ability to work across all layers of the application, from back-end databases to the UI. - Data engineers and data scientists knowledgeable in developing and training data models and building... - Source: Hacker News / almost 4 years ago

What are some alternatives?

When comparing Jupyter and Composable Analytics, 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.

IBM ILOG CPLEX Optimization Studio - IBM ILOG CPLEX Optimization Studio is an easy-to-use, affordable data analytics solution for businesses of all sizes who want to optimize their operations.

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

Pyramid Analytics - Pyramid brings data prep, business analytics, and data science together into one frictionless business and decision intelligence platform that helps you deliver timely and effective decision-making.

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

RapidMiner Studio - Visual workflow designer for predictive analytics that brings data science and machine learning to everyone on the analytics team

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