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Apache Superset VS Matplotlib

Compare Apache Superset VS Matplotlib and see what are their differences

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Contents:
  1. ยป Base Details
  2. ยป Videos
  3. ยป Reviews
  4. ยป Alternatives

Apache Superset logo Apache Superset

modern, enterprise-ready business intelligence web application

Matplotlib logo Matplotlib

matplotlib is a python 2D plotting library which produces publication quality figures in a variety...
  • Apache Superset Landing page
    Landing page //
    2024-09-18
  • Matplotlib Landing page
    Landing page //
    2023-06-14

Apache Superset features and specs

  • Open Source
    Apache Superset is fully open source, allowing users to modify and extend it as needed without any licensing fees.
  • Rich Visualization Options
    Superset offers a wide range of pre-built visualization types, including pie charts, line charts, and maps, allowing for versatile data representation.
  • SQL Lab
    The SQL Lab feature makes it easy to explore and query data in a natural SQL interface, which is highly valuable for analysts and data scientists.
  • Lightweight
    Superset is designed to be a lightweight platform, making it relatively easy to set up and manage compared to more cumbersome BI tools.
  • Extensibility
    With its plugin architecture, Superset can be extended to support additional visualizations and data sources, which makes it highly customizable.
  • Community and Ecosystem
    As part of the Apache Software Foundation, Superset benefits from a robust community and a broad ecosystem of tools and integrations.

Possible disadvantages of Apache Superset

  • Steep Learning Curve
    New users may find it difficult to get started with Superset due to its wide array of features and technical jargon.
  • Limited Documentation
    While there is community-driven documentation, it may not be as comprehensive or up-to-date as needed, posing challenges during troubleshooting.
  • Resource Intensive
    Superset can be resource-intensive and may require significant optimization to run efficiently, especially with large datasets or numerous concurrent users.
  • Basic User Management
    User management features are somewhat basic compared to other BI tools, lacking advanced role-based access control and detailed audit logs.
  • Less Polished UI
    The user interface, while functional, may not be as polished or intuitive as some of the commercial alternatives, impacting the user experience.
  • Scaling Issues
    Superset can face scalability challenges when dealing with massive datasets or a high number of concurrent users, though ongoing improvements are being made.

Matplotlib features and specs

  • Versatility
    Matplotlib can generate a wide variety of plots, ranging from simple line plots to complex 3D plots. This versatility makes it a go-to library for many scientific and technical visualizations.
  • Customization
    It offers extensive customization options for virtually every element of a plot, including colors, labels, line styles, and more, allowing users to tailor plots to meet specific needs.
  • Integrations
    Matplotlib integrates well with other Python libraries such as NumPy, Pandas, and SciPy, making it easier to plot data directly from these sources.
  • Community and Documentation
    It has a large, active community and comprehensive documentation that includes tutorials, examples, and detailed references, which can help users solve problems and improve their plot-making skills.
  • Interactivity
    Matplotlib supports interactive plots, which can be embedded in Jupyter notebooks and GUIs, allowing for dynamic data exploration and presentation.
  • Publication-Quality
    The library is capable of producing high-quality, publication-ready graphics that meet the stringent requirements of academic journals and professional presentations.

Possible disadvantages of Matplotlib

  • Complexity
    While Matplotlib offers extensive customization, it can be complex and sometimes unintuitive for beginners, requiring a steep learning curve to master all its functionality.
  • Performance
    Rendering a large number of plots or handling very large datasets can be slow, making Matplotlib less suitable for real-time data visualization.
  • Modern Aesthetics
    Out-of-the-box plots from Matplotlib can look somewhat dated compared to those from newer plotting libraries like Seaborn or Plotly, requiring additional customization to achieve a modern look.
  • 3D Plots
    Although Matplotlib supports 3D plotting, its capabilities are relatively limited and less sophisticated compared to specialized 3D plotting libraries.
  • Size and Structure
    The package is relatively large and can be slow to import. Its extensive structure can make finding specific functions and understanding the overall architecture challenging.

Analysis of Apache Superset

Overall verdict

  • Apache Superset is a good choice for teams and organizations looking for a flexible, scalable, and user-friendly data visualization tool. It offers a balance between simplicity for non-technical users and depth for advanced users who want to perform complex data analyses. However, it might require some initial setup and configuration, especially for those not familiar with managing web applications or working with databases.

Why this product is good

  • Apache Superset is a powerful, open-source business intelligence tool that provides a wide range of data visualization and exploration capabilities. It is designed to handle large volumes of data, offers an intuitive user interface, and supports a variety of data sources through SQLAlchemy. Its main strengths lie in its ability to create complex dashboards with minimal effort, and its extensibility through a plugin framework. Superset also benefits from a vibrant open-source community, which contributes to its continuous improvement and feature expansion.

Recommended for

  • Organizations with medium to large datasets that need efficient data exploration and visualization.
  • Data analysts and scientists who require a tool that provides powerful SQL capabilities and extensive chart options.
  • Teams looking for an open-source, cost-effective alternative to proprietary business intelligence solutions.
  • Developers who are interested in customizing or extending the platform to fit specific needs via a robust API and plugin system.

Analysis of Matplotlib

Overall verdict

  • Yes, Matplotlib is a good library for data visualization, particularly for users who require a versatile and powerful plotting solution in Python.

Why this product is good

  • Matplotlib is highly regarded due to its extensive customization options, versatility in creating a wide range of static, animated, and interactive plots, and its large user community and support. It integrates well with other scientific libraries in Python, making it a staple for data visualization. The library is also open-source and frequently updated, ensuring it remains a reliable choice for users.

Recommended for

  • Data scientists and analysts needing to create detailed, customized visual representations of their data.
  • Researchers and engineers looking for a comprehensive plotting library that supports scientific and engineering formats.
  • Python developers who require integration with other scientific computing libraries like NumPy and Pandas.

Apache Superset videos

+ Add

Observing Intraday Indicators Using Real-Time Tick Data on Apache Superset and Druid

More videos:

  • Review - Apache Superset-Building Dashboard-Filter or Slicer
  • Review - Installing Apache Superset

Matplotlib videos

+ Add

Learn Matplotlib in 6 minutes | Matplotlib Python Tutorial

Category Popularity

0-100% (relative to Apache Superset and Matplotlib)

Apache Superset

Matplotlib

Business Intelligence
100 100%
Business Intelligence
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Data Dashboard
59 59%
Data Dashboard
41% 41
Technical Computing
0 0%
Technical Computing
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 Apache Superset and Matplotlib

Apache Superset Reviews

Top 10 BI Tools in 2026 (with Pricing, AI Features & Enterprise Fit)
A fintech startup uses Apache Superset to monitor millions of daily transactions. Custom SQL dashboards visualize fraud patterns and transaction anomalies in real time, alerting analysts instantly. This proactive insight enabled faster fraud detection, saving thousands in potential losses quarterly and improving transaction security by 40% within six months.
Source: supaboard.ai
8 Alternatives to Apache Superset Thatโ€™ll Empower Start-ups and Small Businesses with BI
Open-source vs cloud-hosted vs self-hosted Apache Superset open-sourceApache Superset interactive example dashboard. Image source: https://superset.apache.org/Main features and benefits Pricing and offersBest for Main drawbacks Apache Superset alternatives that are suitable for a small business or startup 1. Trevor.ioMain features and benefits Pricing and offersKey...
Source: trevor.io
Top 10 Tableau Open Source Alternatives: A Comprehensive List
Apache Superset is one of the best Tableau Open Source alternatives that you can opt for Data Exploration and Business Analytics. This Open-Source project is licensed under the Apache License 2.0, which allows anyone to use it and distribute a modified version of it. In comparison to Tableau, which charges a minimum of $15 per month for Tableau Viewer, this software is...
Source: hevodata.com
Top 10 Data Analysis Tools in 2022
Apache Superset It is an open-source software application, meaning it can be modified to suit a companyโ€™s needs. It is among the few data analysis tools available to handle big data. Apache Superset is free to use. Apache Superset is a free tool businesses can use to explore and visualize data. However, it does not support NoSQL databases.
Source: www.spiceworks.com

Matplotlib Reviews

25 Python Frameworks to Master
Matplotlib is a widely used tool for data visualization in Python. It provides an object-oriented API for embedding plots into applications.
Source: kinsta.com
5 Best Python Libraries For Data Visualization in 2023
You can use this library for multiple purposes such as generating plots, bar charts, histograms, power spectra, stemplots, pie charts, and more. The best thing about Matplotlib is you just have to write a few lines of code and it handles the rest by itself. Metaplotilib focuses on static images for publication along with interactive figures using toolkits like Qt and GTK.
Source: www.technotification.com
15 data science tools to consider using in 2021
Matplotlib is an open source Python plotting library that's used to read, import and visualize data in analytics applications. Data scientists and other users can create static, animated and interactive data visualizations with Matplotlib, using it in Python scripts, the Python and IPython shells, Jupyter Notebook, web application servers and various GUI toolkits.
Source: searchbusinessanalytics.techtarget.com
Top Python Libraries For Image Processing In 2021
Matplotlib is primarily used for 2D visualizations such as scatter plots, bar graphs, histograms, and many more, but we can also use it for image processing. It is effective to get information out of an image. It doesnโ€™t support all file formats.
Source: www.analyticsvidhya.com
Top 8 Python Libraries for Data Visualization
Matplotlib is a data visualization library and 2-D plotting library of Python It was initially released in 2003 and it is the most popular and widely-used plotting library in the Python community. It comes with an interactive environment across multiple platforms. Matplotlib can be used in Python scripts, the Python and IPython shells, the Jupyter notebook, web application...
Source: www.geeksforgeeks.org

Social recommendations and mentions

Based on our record, Matplotlib should be more popular than Apache Superset. It has been mentiond 114 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.

Apache Superset mentions (62)

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Matplotlib mentions (114)

  • The soul file
    In February, an AI agent named MJ Rathbun submitted a pull request to matplotlib โ€” the Python plotting library used by half the scientific computing world. Scott Shambaugh, a volunteer maintainer, rejected it. Standard code review. Nothing unusual. - Source: dev.to / 5 months ago
  • How to Analyze CSV Files with Python and Pandas
    Numbers are useful, but sometimes itโ€™s easier to spot patterns when you can actually see your data. Pandas works seamlessly with Matplotlib, a popular Python library for creating visualizations. Together, they make it easy to turn raw numbers into clear charts. - Source: dev.to / 8 months ago
  • libmalloc, jemalloc, tcmalloc, mimalloc - Exploring Different Memory Allocators
    We are storing the results in JSON files, which we combine, analyze and visualize using matplotlib in Python. Here's the structure of a benchmark result file:. - Source: dev.to / 9 months ago
  • Building an AI Scoring Agent: Step-By-Step
    NetworkX and Matplotlib were used to visualize the graph structure of the agent. - Source: dev.to / 10 months ago
  • Top 5 GitHub Repositories for Data Science in 2026
    The book introduces the core libraries essential for working with data in Python: particularly IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages Familiarity with Python as a language is assumed; if you need a quick introduction to the language itself, see the free companion project, Aโ€ฆ. - Source: dev.to / 11 months ago
View more

What are some alternatives?

When comparing Apache Superset and Matplotlib, you can also consider the following products

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

Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.

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.

NumPy - NumPy is the fundamental package for scientific computing with Python

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

Seaborn - Seaborn is a Python data visualization library that uses Matplotlib to make statistical graphics.

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