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Amazon AWS VS Jupyter

Compare Amazon AWS VS Jupyter and see what are their differences

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Amazon AWS logo Amazon AWS

Amazon Web Services offers reliable, scalable, and inexpensive cloud computing services. Free to join, pay only for what you use.

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.
  • Amazon AWS Landing page
    Landing page //
    2022-01-29
  • Jupyter Landing page
    Landing page //
    2023-06-22

Amazon AWS features and specs

  • Scalability
    AWS offers highly scalable services, allowing businesses to easily adjust resources based on demand without significant upfront investment.
  • Comprehensive Service Offering
    AWS provides a wide range of services, from compute and storage to machine learning and analytics, catering to diverse business needs.
  • Global Reach
    With data centers located worldwide, AWS enables low-latency access and redundancy, supporting global operations.
  • Strong Security
    AWS has robust security measures, including compliance certifications, encryption, and physical security, ensuring data and infrastructure protection.
  • Pay-as-You-Go Pricing
    AWS offers a flexible pricing model, where users only pay for what they use, helping manage costs effectively.
  • Extensive Integration Options
    AWS integrates with a wide variety of third-party services and APIs, providing seamless integration capabilities for various applications.
  • Innovation
    AWS frequently releases new services and features, staying at the forefront of technology and providing users with cutting-edge tools.

Possible disadvantages of Amazon AWS

  • Cost Management Complexity
    While the pay-as-you-go model offers flexibility, it can be challenging to track and predict costs, especially for large-scale operations.
  • Learning Curve
    AWS has a comprehensive set of services and features, which can be overwhelming for new users to learn and manage effectively.
  • Potential Vendor Lock-In
    Relying heavily on AWS services may result in vendor lock-in, making it difficult to switch providers or migrate workloads in the future.
  • Service Limitations
    Certain AWS services might have limitations or restrictions, which could hinder specific use cases or require workarounds.
  • Support Costs
    AWS offers different support tiers, and premium support options can be expensive for businesses needing immediate and advanced technical assistance.
  • Performance Variability
    Performance can vary based on server load and geographic location, which may affect the consistency and reliability of certain services.
  • Complex Pricing Structure
    AWS's pricing structure can be complicated, with various pricing models and options making it hard to determine the most cost-efficient choice.

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.

Amazon AWS videos

+ Add

Amazon Web Services vs Google Cloud Platform - AWS vs GCP | Difference Between GCP and AWS

More videos:

  • Review - Are AWS Certifications worth it?
  • Review - AWS Certified Solutions Architect Associate Certification Will Get You Paid!

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 Amazon AWS and Jupyter)

Amazon AWS

Jupyter

Cloud Computing
100 100%
Cloud Computing
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Cloud Infrastructure
100 100%
Cloud Infrastructure
0% 0
Data Dashboard
0 0%
Data Dashboard
100% 100

User comments

Share your experience with using Amazon AWS and Jupyter. For example, how are they different and which one is better?
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Reviews

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

Amazon AWS Reviews

  1. macloughlin
    · AV engineer ·
    The best cloud platform out there

    You could say a lot of things about AWS, but among the cloud platforms (and I've used quite a few) AWS takes the cake. It is logically structured, you can get through its documentation relatively easily, you have a great variety of tools and services to choose from [from AWS itself and from third-party developers in their marketplace]. There is a learning curve, there is quite a lot of it, but it is still way easier than some other platforms. I've used and abused AWS and EC2 specifically and for me it is the best.

    👍 Pros:    Great documentation|Website structure visualization|You have control over everything|Flexibility
    👎 Cons:    Learning curve|A lot of dashboards for different things
Top 15 MuleSoft Competitors and Alternatives
API Gateway private endpoints allow AWS customers to use API endpoints inside their VPC. They can leverage Route 53 resolver endpoints and hybrid connectivity to access APIs and integrated backend services from on-premises clients.
Source: bstrategyhub.com
Best Dedicated Server Providers in India: A Comparative Analysis
Dedicated hosts on Amazon EC2 are physical servers that are completely dedicated to meeting corporate compliance standards. With AWS, you can create EC2 instances on a dedicated server. The flexibility offered by Amazon EC2 is definitely one of its biggest advantages, along with high scalability. Apart from that, it isn’t much better than dedicated servers.
Source: moralstory.org
Best Dedicated Server Providers for E-commerce Businesses in India
The dedicated server options from Amazon Web Services (AWS), a well-known brand in the tech industry, are equally excellent. AWS’s elastic infrastructure can smoothly adjust to your demands whether your e-commerce business encounters variable traffic or you expect quick development. AWS guarantees that the speed and performance of your website will always be unmatched thanks...
Source: pricealertbd.com
The Best Dedicated Server Operating System for UK-Based Business
Cloud computing behemoth AWS is renowned for its extensive infrastructure and scalability choices. You can make use of AWS’s numerous data centers, which are positioned strategically to offer low-latency services all across the UK.
Source: featurestic.com
The Best Dedicated Servers for Enterprise Businesses in India: Scalable and Reliable
The extensive selection of cloud-based solutions offered by AWS is one of its main advantages. AWS provides a wide range of cloud services, including computing power, storage choices, databases, machine learning, analytics tools, and dedicated servers. This adaptability enables businesses to create scalable, flexible, and affordable solutions customized to their needs.
Source: india07.in

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, Amazon AWS should be more popular than Jupyter. It has been mentiond 444 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.

Amazon AWS mentions (444)

  • Step-by-Step Guide to Set Up a Cron Job to Run a Report
    Create an AWS Account: If you don’t already have one, sign up at aws.amazon.com. The free tier provides 750 hours per month of a t2.micro or t3.micro instance for 12 months. - Source: dev.to / 2 days ago
  • How to Host an Express App on AWS EC2 with NGINX (Free Tier Guide)
    Sign in to your AWS account. If you’re new to AWS, you can sign up for the free tier to get started without any upfront cost. - Source: dev.to / 27 days ago
  • Understanding AWS Regions and Availability Zones: A Guide for Beginners
    Amazon Web Services (AWS) has completely changed the game for how we build and manage infrastructure. Gone are the days when spinning up a new service meant begging your sys team for hardware, waiting weeks, and spending hours in a cold data center plugging in cables. Now? A few clicks (or API calls), and yes — you've got an entire data center at your fingertips. - Source: dev.to / 21 days ago
  • AWS S3 Storage Classes Explained: Choosing the Right One
    Choosing the right AWS S3 storage class depends on how frequently you access your data and your cost constraints. - Source: dev.to / about 2 months ago
  • Deploy a Django Rest Api on AWS EC2 using Docker, NGINX, Gunicorn and GitHub Action.
    Let’s start by setting up an EC2 instance to deploy our application. To do this, and you’ll need to open an AWS account (if you don’t already have one). - Source: dev.to / 3 months ago
View more

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

What are some alternatives?

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

DigitalOcean - Simplifying cloud hosting. Deploy an SSD cloud server in 55 seconds.

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.

Microsoft Azure - Windows Azure and SQL Azure enable you to build, host and scale applications in Microsoft datacenters.

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

Linode - We make it simple to develop, deploy, and scale cloud infrastructure at the best price-to-performance ratio in the market.

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

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