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Simple Analytics VS Jupyter

Compare Simple Analytics VS Jupyter and see what are their differences

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Simple Analytics logo Simple Analytics

The privacy-first Google Analytics alternative located in Europe.

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.
  • Simple Analytics Landing page
    Landing page //
    2022-09-05

Simple Analytics gives you insights into the performance of your website without ever collecting personal data, with a clean interface, and simple integration. GDPR, CCPA and, PECR compliant because we don't handle personal data and set no cookies.

  • Jupyter Landing page
    Landing page //
    2023-06-22

Simple Analytics features and specs

  • Privacy-focused
    Simple Analytics does not collect personal data, ensuring compliance with privacy laws like GDPR and CCPA. This approach appeals to users concerned about data privacy.
  • Ease of Use
    The platform prides itself on a user-friendly interface, making analytics accessible for individuals with varying levels of technical expertise.
  • No Cookies
    By eliminating the need for cookies, Simple Analytics reduces the complexity of compliance and improves user trust.
  • Transparent Pricing
    Offers straightforward pricing without hidden fees, which benefits small to medium-sized businesses looking for cost-effective solutions.
  • Quick Setup
    Setting up Simple Analytics is a quick process, often taking just a few minutes, reducing the time and effort required to begin tracking site data.
  • Lightweight Script
    The tracking script is lightweight, ensuring that it does not significantly affect website loading times, thus maintaining a good user experience.

Possible disadvantages of Simple Analytics

  • Limited Features
    Compared to more comprehensive platforms like Google Analytics, Simple Analytics offers fewer features and customization options, which may not satisfy advanced users.
  • Basic Reporting
    The reporting capabilities are basic and may not provide in-depth insights that large enterprises or data-driven teams may require.
  • No Integration with Ad Services
    Simple Analytics lacks built-in integrations with advertising services like Google Ads, potentially complicating the tracking of campaign performance.
  • Smaller User Community
    Given its niche market focus, the platform has a smaller user community, which can make it harder to find peer support or community-driven solutions.
  • Less Mature Ecosystem
    Unlike older platforms, Simple Analytics may lack integrations with a wide range of third-party tools and services, limiting its flexibility.
  • Cost
    While the pricing is transparent, it can still be seen as relatively high for the features offered, especially when compared to free alternatives like Google Analytics.

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 Simple Analytics

Overall verdict

  • Simple Analytics is a good choice for users who prioritize privacy and simplicity in their web analytics tools. It provides sufficient insights for basic website analytics needs without overwhelming users with too much data or complex features.

Why this product is good

  • Simple Analytics is often praised for its privacy-focused approach. It does not collect personal data, which appeals to users and businesses concerned about privacy and compliance with data protection regulations like GDPR. The platform offers an easy-to-understand interface with essential analytics metrics, making it accessible to users without a technical background. Additionally, Simple Analytics is lightweight, which means it doesn't slow down websites as much as other analytics tools might.

Recommended for

    Simple Analytics is recommended for small to medium-sized businesses, bloggers, and website owners who need straightforward analytics and value privacy. It’s particularly suitable for those looking to comply with privacy regulations without compromising on user data protection.

Simple Analytics videos

+ Add

Fathom, simple analytics. A Google Analytics alternative | Privacy & Simplicity focused! 🎯

More videos:

  • Review - Seriously Simple Analytics Review
  • Review - Seriously Simple Analytics Review
  • Demo - Why we created Simple Analytics

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 Simple Analytics and Jupyter)

Simple Analytics

Jupyter

Analytics
100 100%
Analytics
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Web Analytics
100 100%
Web Analytics
0% 0
Data Dashboard
0 0%
Data Dashboard
100% 100

User comments

Share your experience with using Simple Analytics 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 Simple Analytics and Jupyter

Simple Analytics Reviews

Top 10 AI Data Analysis Tools in 2024
Simple Analytics is a revolutionary web analytics platform that prioritizes user privacy and transparency above all else. Developed as an ethical alternative to data-hungry giants like Google Analytics, Simple Analytics offers a refreshingly lightweight and user-friendly solution for tracking website metrics without compromising on data protection. With its unwavering...
Source: powerdrill.ai
Privacy-oriented alternatives to Google Analytics
Simple Analytics was my original second contender for the analytics of this blog. The $19 a month starting plan with 100k pageviews is on the more expensive side, but their yearly deal gets you a better price than Fathom at just $9 a month.
Source: nts.strzibny.name
Lightweight alternatives to Google Analytics
One is the minimalist Simple Analytics product, which is a cloud-based tool created by solo developer Adriaan van Rossum; it has a clean-looking interface with only the few key metrics, similar to Plausible. Another is Fathom, which was open source initially, but the current version is proprietary (although the company hopes to start maintaining the open-source code base...
Source: lwn.net

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 should be more popular than Simple Analytics. 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.

Simple Analytics mentions (26)

  • This Next.js blog template is awesome.
    Multiple analytics options including Umami, Plausible, Simple Analytics, Posthog and Google Analytics. - Source: dev.to / 8 months ago
  • Awesome-no-code-tools
    Simple Analytics - Simple, clean, and friendly analytics. - Source: dev.to / 11 months ago
  • SaasRock v0.5.0 - Cookie consent and built-in Analytics
    SaasRock does not intend to invent the wheel, there are great analytics solutions out there, both free and powerful. But SaasRock’s main goal is to have everything you need when building SaaS applications, at least in a minimal way. - Source: dev.to / almost 3 years ago
  • Italian watchdog bans use of Google Analytics
    Regarding forbidden countries, it’s not forbidden in the Netherlands, yet. They will announce a verdict in a form of a report by the end of 2022 [1]. To give people an option and pink something else over Google Analytics, I have built an alternative, Simple Analytics [2]. It doesn’t use cookies or any form of tracking and you get still the useful data that 80% of the website owners need. [1]... - Source: Hacker News / almost 3 years ago
  • Italian watchdog bans use of Google Analytics
    It is. Most startups in the EU have to use more and more businesses in the EU. The selection is little, so way more changes to succeed if your EU based and serve both markets. I run Simple Analytics [1], which is a privacy-first analytics business from the Netherlands. I see a lot of business from the EU just because we are from the EU as well. [1] https://simpleanalytics.com/?ref=hn. - Source: Hacker News / almost 3 years 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 / 3 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 / 4 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 / 5 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
View more

What are some alternatives?

When comparing Simple Analytics and Jupyter, you can also consider the following products

Plausible.io - Plausible Analytics is a simple, open-source, lightweight (< 1 KB) and privacy-friendly web analytics alternative to Google Analytics. Made and hosted in the EU, powered by European-owned cloud infrastructure 🇪🇺

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.

Fathom Analytics - Simple, trustworthy website analytics (finally)

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

Google Analytics - Improve your website to increase conversions, improve the user experience, and make more money using Google Analytics. Measure, understand and quantify engagement on your site with customized and in-depth reports.

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

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