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Heroku Enterprise VS Jupyter

Compare Heroku Enterprise VS Jupyter and see what are their differences

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Heroku Enterprise logo Heroku Enterprise

Heroku Enterprise is a flexible IT management for developers that lets them build apps using their preferred languages and tools like Ruby, Java, Python and Node.

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.
  • Heroku Enterprise Landing page
    Landing page //
    2023-01-23
  • Jupyter Landing page
    Landing page //
    2023-06-22

Heroku Enterprise features and specs

  • Scalability
    Heroku Enterprise offers robust tools for scaling applications easily. You can add more compute resources with just a few clicks, making it simpler to handle traffic spikes and growing user bases.
  • Ease of Use
    Heroku is known for its developer-friendly environment, which simplifies deployment and management of applications. The platform abstracts much of the underlying infrastructure complexity, allowing developers to focus more on coding.
  • Integration
    Heroku Enterprise integrates smoothly with other Salesforce services and third-party tools, providing versatility and extending the capabilities of your applications.
  • Security
    Heroku Enterprise offers enhanced security features such as private spaces, TLS encryption, and compliance with industry standards (e.g., HIPAA, PCI). It ensures that enterprise-level security requirements are met.
  • Support
    Heroku Enterprise clients receive premium support services, including 24/7 customer service, which ensures that any technical issues are resolved quickly and efficiently.

Possible disadvantages of Heroku Enterprise

  • Cost
    Heroku Enterprise can be quite expensive, especially for smaller companies or startups. The pricing structure might be prohibitive for some organizations.
  • Limited Control
    While the ease of use is a strong point, it also means less control over the underlying infrastructure. This can be a drawback for businesses with specific configurations or those requiring deep infrastructure customizations.
  • Performance
    Despite its strong scalability features, some users report that Heroku applications can experience latency issues under heavy load, which might affect performance.
  • Vendor Lock-in
    Relying heavily on Heroku Enterprise for application deployment could pose a risk of vendor lock-in, making it challenging to migrate to other platforms in the future.
  • Customization Limitations
    While Heroku offers numerous add-ons and integrations, it still has limitations in terms of customization compared to managing your own infrastructure, which could be a disadvantage for highly specialized applications.

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.

Analysis of Heroku Enterprise

Overall verdict

  • Heroku Enterprise is a solid choice for organizations that need a reliable and developer-friendly platform to support their application development lifecycle. It balances simplicity with the advanced features required by enterprises, making it suitable for handling complex projects and larger teams.

Why this product is good

  • Heroku Enterprise is generally considered a good option for businesses looking for a robust platform-as-a-service (PaaS) to deploy, manage, and scale their applications. Key reasons include its ease of use, strong support for a wide range of programming languages, seamless integration with popular development tools, and a comprehensive set of features tailored for enterprise needs, such as enhanced security, compliance, and monitoring capabilities.

Recommended for

    Heroku Enterprise is recommended for mid-sized to large businesses, startups experiencing rapid growth, and teams that value streamlined deployment processes, scalability, and integration with other cloud services. It is particularly well-suited for developers who prefer a platform that abstracts much of the underlying infrastructure management, allowing them to focus on code and innovation.

Heroku Enterprise videos

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Jupyter videos

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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 Heroku Enterprise and Jupyter)

Heroku Enterprise

Jupyter

Monitoring Tools
100 100%
Monitoring Tools
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Backup & Restore
100 100%
Backup & Restore
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 Heroku Enterprise and Jupyter

Heroku Enterprise Reviews

We have no reviews of Heroku Enterprise yet.
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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.

Heroku Enterprise mentions (0)

We have not tracked any mentions of Heroku Enterprise yet. Tracking of Heroku Enterprise 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 / 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 / 9 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 / 12 months ago
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What are some alternatives?

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

ManageEngine RecoveryManager Plus - RecoveryManager Plus is one such enterprise backup solution which has the ability to easily backup and restores both the domain controllers and virtual machines.

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.

SECDO - SECDO offers automated endpoint security and incident response solutions

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

Traverse Monitoring - Traverse Monitoring is an IT Management software that provides businesses with a network monitoring solution which is capable of handling the tasks of monitoring private clouds, distributed network infestation and virtualized infrastructure.

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

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