Analysis and output tools are crucial components in the data analytics pipeline, enabling users to derive insights from processed data and present them in an understandable format. These tools range from programming frameworks for custom visualizations to no-code platforms for quick and easy chart creation.
They can be broadly categorized into five main areas:
- Framework: Libraries available for creating visualizations
- High-code: Interfaces requiring substantial coding to create visualizations
- Low-code: Applications requiring little development but some technical skills
- No-code: Drag-and-drop interfaces requiring no programming skills
- Web Analytics: Tools for analyzing website and user behavior data
Here is a summary table of the main analysis and output tools we have identified, sorted by subcategory and alphabetical order.
Framework
| Tool |
Subcategory |
Creation Date |
Stars |
Forks |
Contributors |
Last Release |
Latest Commit |
Meets Criteria* |
Link |
| Apache ECharts |
Framework |
03/04/2013 |
64619 |
19770 |
254 |
30/07/2025 |
16/09/2025 |
Yes |
https://github.com/apache/echarts |
| Bokeh |
Framework |
26/03/2012 |
20095 |
4235 |
385 |
N/A |
17/09/2025 |
Yes |
https://github.com/bokeh/bokeh |
| Chart JS |
Framework |
17/03/2013 |
66545 |
11967 |
427 |
14/06/2025 |
16/09/2025 |
Yes |
https://github.com/chartjs/Chart.js |
| Cube |
Framework |
16/09/2018 |
18889 |
1891 |
336 |
17/09/2025 |
17/09/2025 |
Yes |
https://github.com/cube-js/cube |
| D3JS |
Framework |
27/09/2010 |
111480 |
22835 |
135 |
12/03/2024 |
27/07/2025 |
Yes |
https://github.com/d3/d3 |
| Holoviz |
Framework |
22/09/2017 |
890 |
126 |
23 |
22/03/2023 |
21/04/2025 |
No |
https://github.com/holoviz/holoviz |
| Matplotlib |
Framework |
19/02/2011 |
21700 |
8028 |
419 |
29/08/2025 |
17/09/2025 |
Yes |
https://github.com/matplotlib/matplotlib |
| Plotly |
Framework |
21/11/2013 |
17740 |
2710 |
260 |
12/08/2025 |
27/08/2025 |
Yes |
https://github.com/plotly/plotly.py |
| Seaborn |
Framework |
18/06/2012 |
13441 |
2034 |
190 |
25/01/2024 |
10/07/2025 |
Yes |
https://github.com/mwaskom/seaborn |
| Vega/Altair |
Framework |
19/09/2015 |
10011 |
819 |
159 |
24/11/2024 |
03/09/2025 |
Yes |
https://github.com/vega/altair |
High-Code
| Tool |
Subcategory |
Creation Date |
Stars |
Forks |
Contributors |
Last Release |
Latest Commit |
Meets Criteria* |
Link |
| Apache Zeppelin |
High-code |
22/11/2013 |
6382 |
2807 |
332 |
N/A |
22/09/2024 |
Yes |
https://github.com/apache/zeppelin |
| Plotly Dash |
High-code |
05/06/2017 |
21230 |
2048 |
138 |
12/09/2024 |
20/09/2024 |
Yes |
https://github.com/plotly/dash |
| JupyterLab |
High-code |
24/04/2017 |
14103 |
3329 |
362 |
26/08/2024 |
26/09/2024 |
Yes |
https://github.com/jupyterlab/jupyterlab |
| Observable |
High-code |
29/03/2018 |
992 |
71 |
12 |
06/08/2024 |
06/08/2024 |
No |
https://github.com/observablehq/runtime |
| Panel |
High-code |
05/07/2018 |
4690 |
508 |
177 |
03/10/2024 |
03/10/2024 |
Yes |
https://github.com/holoviz/panel |
| Py Shiny |
High-code |
15/12/2020 |
1246 |
72 |
25 |
03/09/2024 |
04/10/2024 |
Yes |
https://github.com/posit-dev/py-shiny |
| Rill |
High-code |
15/06/2021 |
5123 |
247 |
52 |
24/10/2024 |
06/11/2024 |
Yes |
https://github.com/rilldata/rill |
| Streamlit |
High-code |
28/09/2018 |
34812 |
3125 |
238 |
27/08/2024 |
26/09/2024 |
Yes |
https://github.com/streamlit/streamlit |
| Taipy |
High-code |
01/06/2022 |
12508 |
938 |
48 |
06/10/2024 |
07/10/2024 |
Yes |
https://github.com/Avaiga/taipy |
Low-Code
| Tool |
Subcategory |
Creation Date |
Stars |
Forks |
Contributors |
Last Release |
Latest Commit |
Meets Criteria* |
Link |
| Evidence |
Low-code |
01/03/2022 |
4187 |
198 |
53 |
25/09/2024 |
26/09/2024 |
Yes |
https://github.com/evidence-dev/evidence |
| Grafana |
Low-code |
27/11/2013 |
64114 |
11997 |
379 |
26/09/2024 |
26/09/2024 |
Yes |
https://github.com/grafana/grafana |
| Kibana |
Low-code |
13/05/2013 |
19752 |
8149 |
345 |
10/09/2024 |
26/09/2024 |
Yes |
https://github.com/elastic/kibana |
| KNIME |
Low-code |
14/04/2006 |
557 |
131 |
54 |
N/A |
25/09/2024 |
Yes |
https://github.com/knime/knime-core |
| Orange |
Low-code |
25/10/2012 |
4805 |
1000 |
101 |
27/05/2024 |
26/09/2024 |
Yes |
https://github.com/biolab/orange3 |
| RAWGraphs |
Low-code |
23/01/2013 |
8651 |
1840 |
16 |
26/01/2024 |
26/01/2024 |
No |
https://github.com/rawgraphs/rawgraphs-app |
| Redash |
Low-code |
13/11/2013 |
26028 |
4344 |
393 |
24/11/2021 |
19/09/2024 |
No |
https://github.com/getredash/redash |
No-Code
| Tool |
Subcategory |
Creation Date |
Stars |
Forks |
Contributors |
Last Release |
Latest Commit |
Meets Criteria* |
Link |
| Chartbrew |
No-code |
19/03/2021 |
3926 |
414 |
109 |
22/10/2024 |
22/10/2024 |
No |
https://github.com/chartbrew/chartbrew |
| Lightdash |
No-code |
19/03/2021 |
3926 |
414 |
109 |
22/10/2024 |
22/10/2024 |
Yes |
https://github.com/lightdash/lightdash |
| Metabase |
No-code |
15/10/2014 |
38232 |
5065 |
379 |
24/09/2024 |
26/09/2024 |
Yes |
https://github.com/metabase/metabase |
| Superset |
No-code |
19/11/2015 |
61954 |
13585 |
424 |
22/08/2024 |
26/09/2024 |
Yes |
https://github.com/apache/superset |
Web Analytics
| Tool |
Subcategory |
Creation Date |
Stars |
Forks |
Contributors |
Last Release |
Latest Commit |
Meets Criteria* |
Link |
| Matomo |
Web Analytics |
30/03/2011 |
20844 |
2757 |
334 |
13/09/2025 |
17/09/2025 |
Yes |
https://github.com/matomo-org/matomo |
| Plausible |
Web Analytics |
04/12/2018 |
23345 |
1274 |
106 |
11/04/2025 |
17/09/2025 |
Yes |
https://github.com/plausible/analytics |
| Posthog |
Web Analytics |
23/01/2020 |
29252 |
1931 |
331 |
17/09/2025 |
17/09/2025 |
Yes |
https://github.com/PostHog/posthog |
*Criteria: >40 contributors, >500 stars, and recent releases/commit
Framework
- Apache ECharts: A powerful, interactive charting and data visualization library for browser.
- Bokeh: A versatile library for creating interactive visualizations for modern web browsers.
- Chart JS: Simple yet flexible JavaScript charting library for designers & developers.
- Cube: A headless BI platform that enables data engineers to transform raw data into consistent definitions and data analysts to accelerate development of analytical applications.
- D3JS: A JavaScript library for producing dynamic, interactive data visualizations in web browsers, offering great control over the final visual result.
- Holoviz: A coordinated set of Python tools for data analysis and visualization in Jupyter notebooks.
- Matplotlib: A comprehensive library for creating static, animated, and interactive visualizations in Python.
- Plotly: A graphing library that makes interactive, publication-quality graphs online.
- Seaborn: A Python data visualization library based on matplotlib that provides a high-level interface for drawing attractive and informative statistical graphics.
- Vega/Altair: A declarative statistical visualization library for Python, based on Vega and Vega-Lite.
High-Code
- Apache Zeppelin: A web-based notebook that enables data-driven, interactive data analytics and collaborative documents with SQL, Scala and more.
- Plotly Dash: A productive Python framework for building web analytic applications.
- JupyterLab: A web-based interactive development environment for Jupyter notebooks, code, and data.
- Observable: A magic notebook for exploring data and thinking with code, designed for collaboration and fast iteration.
- Panel: A high-level app and dashboarding solution for Python that enables easy composition of widgets, plots, tables, and other components.
- Py Shiny: A Python port of the popular R Shiny framework, allowing for the creation of interactive web applications straight from Python.
- Rill: An open-source tool for instant cloud data applications, combining data modeling with analytics capabilities.
- Streamlit: An open-source app framework for Machine Learning and Data Science teams to create beautiful, performant web apps in pure Python.
- Taipy: A Python library for building complete data-driven web applications. It combines data pipeline management with GUI creation capabilities.
Low-Code
- Evidence: A code-based business intelligence tool that generates interactive dashboards from SQL queries and markdown.
- Grafana: An open-source platform for monitoring and observability, allowing you to query, visualize, alert on and understand your metrics.
- Kibana: A free and open user interface that lets you visualize your Elasticsearch data and navigate the Elastic Stack.
- KNIME: An open-source data analytics platform that allows users to create visual workflows with an intuitive drag-and-drop interface.
- Orange: An open-source machine learning and data visualization toolbox featuring interactive data analysis workflows with a large toolbox.
- RAWGraphs: An open-source data visualization framework to create custom vector-based visualizations.
- Redash: A tool to connect and query your data sources, build dashboards to visualize data and share them with your company.
No-Code
- Chartbrew: A self-hosted data visualization and analytics platform that allows users to connect multiple data sources, create interactive charts, and build custom dashboards without writing code.
- Lightdash: Open-source BI tool built specifically for dbt, combining the power of data modeling with intuitive analytics and visualization.
- Metabase: An easy-to-use, open-source solution for business intelligence, dashboards, and data visualization.
- Superset: A modern data exploration and visualization platform that is fast, lightweight, intuitive, and loaded with options.
Web Analytics
- Matomo: An open-source alternative to Google Analytics that puts privacy first while providing comprehensive web analytics capabilities and marketing tools.
- Plausible: A lightweight, open-source web analytics tool focused on privacy and simplicity, offering essential website traffic metrics without compromising user data.
- Posthog: An open-source product analytics platform that helps you understand user behavior, with features for tracking events, creating funnels, and analyzing user flows.
When choosing a tool, consider factors such as:
- Level of customization required
- Technical skills of your team
- Types of visualizations needed
- Integration with existing data stack
- Privacy and data ownership requirements
- Real-time analytics needs
- Scale of data and user base
- Cost and licensing considerations
Different tools can be combined to create a comprehensive analytics stack:
- Use Frameworks for custom, complex visualizations
- Deploy High-Code solutions for specialized analytical applications
- Implement Low-Code tools for team dashboards and monitoring
- Utilize No-Code platforms for business user self-service
- Leverage Web Analytics for understanding user behavior and engagement
Remember that the choice of analysis and output tools can significantly impact how effectively you communicate your data insights. It’s often beneficial to have a mix of tools available to address different visualization needs and user skill levels within your organization.
The Challenge of Choice
The open-source community has developed numerous solutions for various aspects of data handling, including: