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Redash VS Jupyter

Compare Redash VS Jupyter and see what are their differences

Redash logo Redash

Data visualization and collaboration tool.

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.
  • Redash Landing page
    Landing page //
    2023-07-22
  • Jupyter Landing page
    Landing page //
    2023-06-22

Redash features and specs

  • Open Source
    Redash is an open-source tool, allowing users to customize and extend its functionalities to suit their specific needs.
  • Cost
    As an open-source product, Redash can be used for free, making it cost-effective for organizations with limited budgets.
  • Data Source Integration
    Redash supports a wide range of data sources, including SQL databases, NoSQL databases, and cloud services, making it versatile for different data needs.
  • Query Editor
    Redash comes with a powerful query editor that supports SQL, which makes it easy for data analysts to write and execute queries.
  • Visualization Options
    Redash provides multiple visualization options such as bar charts, line charts, and pie charts to help users interpret data effectively.
  • Collaboration
    Redash allows multiple users to collaborate on queries and dashboards, fostering teamwork within organizations.
  • Alerting
    Users can set up alerts to notify them when certain data conditions are met, enabling proactive decision-making.

Possible disadvantages of Redash

  • User Interface
    The user interface of Redash can be less intuitive, especially for new users who are not familiar with data analytics tools.
  • Scalability
    Redash might face performance issues when dealing with very large datasets or a high number of simultaneous queries.
  • Community Support
    Being an open-source product, Redash relies heavily on community support, which can be inconsistent and slower compared to commercial products with dedicated support teams.
  • Advanced Features
    Compared to more established BI tools, Redash may lack some advanced features and functionalities like detailed user access controls and more complex data transformations.
  • Documentation
    The documentation for Redash can be lacking or outdated, making it challenging for users to find the information they need.
  • Deployment Complexity
    Setting up and maintaining a Redash instance can be complex and require a good understanding of infrastructure management.

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 Redash

Overall verdict

  • Yes, Redash is considered good for users who need a straightforward, yet powerful, tool for data visualization and exploration. Its ease of use, combined with the capabilities to support various data sources, makes it a solid choice for companies and data teams.

Why this product is good

  • Redash is well-regarded for its simplicity and powerful visualization capabilities. It is an open-source platform that allows users to connect to a wide range of data sources, create dashboards, and share insights easily. It provides users with the flexibility to write SQL queries to fetch data and then visualize it in an interactive and intuitive manner. Redash's support for multiple data source connections, along with its collaborative features, makes it a great tool for teams looking to leverage data efficiently.

Recommended for

  • Data Analysts
  • Business Intelligence Teams
  • Organizations looking for an open-source data visualization tool
  • Teams needing collaboration features for data-driven decision making
  • Users with SQL knowledge needing flexible query capabilities

Redash videos

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Jupyter videos

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 Redash and Jupyter)
Data Dashboard
44 44%
56% 56
Data Science And Machine Learning
Business Intelligence
100 100%
0% 0
Data Visualization
100 100%
0% 0

User comments

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Reviews

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

Redash Reviews

6 Best Looker alternatives
Accessibility: Though it also requires support from your data team, Looker is more targeted to non-tech users than Redash, since Redash requires SQL expertise.
Source: trevor.io
Best 8 Redash Alternatives in 2023 [In Depth Guide]
So all-in-all, Redash is meant for users who have the technical knowledge and depend a lot on KPIs, and Datapad is for users and businesses who just want an overview of KPI performance but quickly.
Source: www.datapad.io
8 Alternatives to Apache Superset That’ll Empower Start-ups and Small Businesses with BI
Small businesses and startups with limited resources that need to answer simple queries will find Metabase, Tableau, and PowerBI suitable for their needs. However, if you have an in-house data team dedicated to the project, you might find open-source software like Redash and Metabase (open-source version) beneficial. And if you have the team, time, and money, Looker or...
Source: trevor.io
Top 10 Tableau Open Source Alternatives: A Comprehensive List
With Redash, you can integrate with Data Warehouses more quickly, write SQL queries to pull subsets of data for visualizations, and share dashboards more easily. Its SQL interface is especially easy to use for anyone who is familiar with SQL Server Management Studio or any querying GUI tool for databases. It also provides support for over 20+ data sources and allows users to...
Source: hevodata.com

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.
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.

Social recommendations and mentions

Based on our record, Jupyter seems to be a lot more popular than Redash. While we know about 216 links to Jupyter, we've tracked only 19 mentions of Redash. 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.

Redash mentions (19)

  • Tool or service for querying and exposing database through API
    I am looking for service or tool similiar to Metabase or Redash that allows me to add data source - for example Postgres connection, and create raw SQL queries that can be shared or exposed through API. So instead of keeping raw SQL code somewhere, my other service would call this tool e.g. http://microservice/query=1?param1=xx&page=2 and get the results from the DB. These calls are internal only and part of ETL... Source: almost 2 years ago
  • Did anyone try Openblocks for multi-tenant client reporting?
    I have tried Metabase, Redash beore (both self hosted open source versions), from my experience I find Metabase a bit easy to work with. Source: almost 2 years ago
  • Best apps for transitioning from Spreadsheets to SQLite?
    Regarding visualization tools, sqliteviz has proven to be the best I've found so far. Their web app runs locally but has some trackers, so I run it locally via a simple, static HTTP server. Falcon and Redash seem like overkill for my needs. Source: about 2 years ago
  • Framework Laptops are now Thunderbolt 4 certified
    In addition to metabase there are redash[0] and apache superset[1]. They are more or less similar to metabase with some different quirks. You can also visualize quite a bit of data in grafana[2] as well. [0] https://redash.io/ [1] https://superset.apache.org/ [2] https://github.com/grafana/grafana. - Source: Hacker News / over 2 years ago
  • How to program an appealing data visualization, that automatically synchronizes itself? (Picture in comments)
    This is typically called a "dashboard" and there is a whole industry of existing commercial products (for example https://redash.io/) that are built around doing data analysis and visualization. Source: over 2 years ago
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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 / 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 Redash and Jupyter, you can also consider the following products

Microsoft Power BI - BI visualization and reporting for desktop, web or mobile

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.

Tableau - Tableau can help anyone see and understand their data. Connect to almost any database, drag and drop to create visualizations, and share with a click.

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

Metabase - Metabase is the easy, open source way for everyone in your company to ask questions and learn from...

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