Data visualization tools are used to represent information with graphs, charts, tables, maps, and other detailed visuals.
- Best Open-Source Data Visualization Tools
- How Do You Evaluate A Data Visualization Tool?
They usually combine data presentation and analysis to help professionals in making more informed decisions in data-driven fields (such as engineering, data science, and business analysis).
Choosing the right data visualization tool will help you to reduce data errors and save enormous time and resources in your workflow. And, choosing open-source options would offer you considerable freedom for customizing the tool in question to suit your particular needs.
Therefore, here, I am presenting to you the best open-source tools for data visualization that I can find:
Best Open-Source Data Visualization Tools
Redash is a cloud-based and open-source data visualization and analytics tool. It runs on an SQL server and sports an online SQL editor. The tool has both hosted and open-source/self-hosted versions. But the hosted version is going to be shut down by November 30, 2021.
You can use Redash as follows:
- To build dashboards for presenting data.
- To connect with various data sources, gather information from them, and also query them intelligently. It can also blend the gathered data into a single unit for easy access to every team member.
- That reminds me that Redash can also be used to share data with team members while enabling collaborative efforts. Reports and visualizations can be shared in various ways such as URLs, web embeds, emails, and more.
The above features apart, I consider Redash as one of the best open-source data visualization and analytics tools for the following reasons:
- It offers a versatile range of options for data visualization. Some of these options are pie charts, bar charts, tables, line graphs, pivots, cohorts, scatter graphs, maps, and many more. You can also use it to set up alerts and get notifications on events that may serve as a data source.
- Ease of Use: With the intuitive SQL client and interface that Redash sports, users can easily browse schema, create snippets, call up and insert queries, and do a lot of other things.
- Ease of access: Being cloud-based, Redash can be used from anywhere there is an internet connection.
- Redash offers a wide range of customization options due to its open-source architecture. You can even tweak its features from the code levels if you have the expertise.
- Users can deploy pre-configured key performance indicators to generate reports.
- Its single sign-on capability and other access control functionalities make Redash one of the most secure data visualization tools in the market.
- Apart from the main features of Redash, you can extend its functions in several other ways by the use of APIs.
You can get Redash on GitHub here.
Charted is an open-source data visualization tool that runs on the MIT license. It was originally developed by the blogging platform Medium.com.
I can say that the mainstay of Charted is the automatic visualization of data. All you need is to provide a link to a data file and the system turns up a comprehensive, well-choreographed, and easily accessible set of data from it.
Here are other reasons why I consider charted as one of the best open-source data visualization solutions in the market:
- The platform is purposely built with ease of use in mind. For example, the interface assembles only essential features. Thus, it is considerably unencumbered. I also think this is because most of the functions are already automated.
- It presents its results equally well on various screen sizes.
- It updates its charts regularly (at 30-minute intervals).
- It is great at sorting data by taking data series and charts apart.
- Using Charted, you can also sort available data by adjusting them according to types, backgrounds, titles or labels, and more.
- Its file support is also fairly comprehensive. It includes such file types as comma-separated values (CSV), tab-separated values (TSV), Dropbox share links, and Google Sheets.
3. Grafana Labs
Grafana Labs is an open-source data visualization and analytics tool that is distributed on the AGPL 3.0 license. I consider it not only to be one of the best open-source options but also one of the best in the data analytics sector.
Here are my main reasons for including Grafana on this list:
- You can use the tool to access data almost anywhere.
- Once you have accessed the data, you can easily use Grafana to also visualize and query it with ease.
- The tool is built with robust collaborative features. Thus, you can use it to easily create dynamic and reusable dashboards. Then, you can share the dashboards with your team members.
- Grafana is great for blending pieces of data from various sources. For example, this feature enables you to use the tool for presenting information from different data sources on the same graph.
- You can equally use the tool to get live notifications from data sources. Its alert system also integrates with a wide range of systems such as PagersDuty, Slack, and OpsGenie.
- It ensures efficient management of your logs, with the ability to wiggle between metrics and logs with considerable ease.
- One of the features I like most in Grafana is the ease with which you can use it to compare different results on a single interface. It makes data association almost like a stroll in the park.
Here is the Grafana repository on GitHub.
I think that D3 is one of the best open-source data visualization solutions in the market currently for the following reasons:
- It offers users the absolute freedom to copy, use, modify, and distribute it. The only requirement is that you acknowledge the D3 copyright wherever the software is distributed. Users are even allowed to accept fees for sharing the software. This is based on the Internet Systems Consortium’s free permissive software license on which the software runs.
- D3’s flexibility is second to none. It offers users the opportunity to call up arbitrary data and use it to transform a document. For instance, you can use it to create an HTML table from a jumble of data by connecting it with the system’s Document Object Model (DOM).
- D3 is unique for being able to make data visualization available on web browsers.
The software’s open-source repository is here on GitHub.
5. Google Charts
Google Charts is an open-source data visualization tool provided as a web service by Google Inc. It is unique for its ability to throw up clean and interactive graphical charts from data sets supplied by the users.
Here are my reasons for considering Google Charts as one of the best data visualization software in the market presently:
- It offers one of the richest options for data outputting. There are over 100 of them, ranging from simple bar charts and line graphs to symbol annotation, buckle axis, stacked area, date timeline point, and so many more.
- Google Charts is specially built for easily displaying interactive data outputs on web pages. You can link up to the source from the web page by clicking on an auto-generated hyperlink.
- You can customize data output to match the feel of your website.
- Google Charts is available to users absolutely free of charge.
- It offers the capability for real-time connection to data sources.
- It benefits from Google’s single sign-on and other security features and functionalities.
Find Google Charts on Github here.
One of the best open-source data visualization tools in the market right now, dygraphs is best suited for creating interactive charts.
You can hover a cursor over individual values to highlight them. You can also zoom in on any point by clicking and dragging with your mouse. Again, you can pan out and relate with data strings in several other ways.
Other reasons why I consider this tool as one of the best open-source data visualization software programs today are:
- It is well suited for handling a huge array of data.
- Dygraphs offers so many customization options.
- It works across all devices, from desktop to tablets and mobile.
Here is the dygraphs repository on GitHub.
RapidMiner is a suite of software programs on the cloud. The entire suite is used for shoring up a sequential data analytics environment. In-depth data visualization is only a part of the suite.
There are many reasons why I think that RapidMiner is one of the most favored open-source data visualization solutions out there right now. Here they are:
- Ease of use. RapidMiner sports an intuitive and unified user interface complete with drag and drop controls. Thus, you can use it to work on huge data sets without touching a single line of code.
- RapidMiner is deeply automated. For example, it executes tasks within a database via a range of looping processes powered by artificial intelligence, machine learning, and predictive models. This way, many operations are carried out without repeated human input.
- Thus, RapidMiner reduces data errors while saving enormous workflow time and resources.
- RapidMiner visualization digs deep into your data set through its interactive charts and graphs. There are many ways to interact with the output – including zooming, panning, and more.
- There are so many data types that you can work with on RapidMiner. They include text, graphics, multimedia, and many more. I learned there are over 40 of them.
Here is the entire RapidMiner suite on GitHub.
Like Google Charts, Chart,js is yet another service you can use to insert clean data outputs (such as charts and graphs) into your web pages.
It has many other features that make me consider it as one of the best open-source data visualization tools in the market as I write this. Here they are:
- This is one of the few tools that can mix data outputs. For example, it can combine bar charts and line graphs in a unit of output. This way, you can easily compare and contrast details of the same data set.
- Data outputs are presented in animated formats that make information come alive. This includes transitions, effects, and more.
- You can customize the data output to your taste.
- The output is also responsive to screen size.
9. RAW Graphs
Built on D3.js, RAWGraphs makes data sourcing and visualization extremely easy. Here are other features and functionalities of this tool that merits it a place among the best open-source data visualization tools of today:
- It has many ways to source and displays data automatically. For example, you can input comma-separated values, tab-separated values, or simply copy and paste from a spreadsheet, and watch RAWGraphs transform it into beautiful output.
- It displays data using a wide variety of graphic models. These include both conventional (like pie/bar charts and line graphs) and unconventional ones.
- With RAWGraphs, you can have a deeper knowledge of your data set by exploring its patterns and trends. You can achieve this by mapping the various dimensions of your data set with the use of visual variables.
- You can export and edit your output anywhere as vector or raster images.
RAWGraphs is deployed as an open-source web app on the Apache 2.0 license. You can contribute to it here on GitHub.
Datawrapper is yet another open-source data visualization tool I highly recommend. It is free to use.
This tool has many features and functionalities that enable easy data sourcing, display, and analysis. Here are some of them:
- You don’t need to sign up to use Datawrapper.
- Every user on the platform has the opportunity to create an unlimited number of visualizations.
- The output is responsive and viewable on any screen, including mobile screens.
- You can co-view a visualization with your team members and work together on it.
- Datawrapper visualizations can be scaled to any audience size.
- There is a wide range of options for exporting visualizations. They range from vector and scalar images to printable PDFs.
Find Datawrapper on GitHub here.
Interesting Comparison: Tableau Vs Power BI
- It enables you to view interactive and dynamic maps and charts on web browsers.
- It allows you to present visualizations in so many ways such as vector and scalar images as well as cartography.
- It is easy to publish your visualization due to Polymaps’ use of spherical Mercator tile format.
Kibana is an open-source data visualization software that was built specifically for the Amazon Elasticsearch engine. But it can also run in other environments.
My main reason for including Kibana on this list of the best open-source data visualization tools in the market currently is its ease of use. The interface is quite intuitive and does not require much technical knowledge to master. It is also relatively easy to create, access, and share visualization dashboards using this tool.
My other reasons for recommending the software are:
- It offers very in-depth and interactive reporting tools.
- It is built to be able to handle huge amounts of data.
- You can use it to present continuous layers of information for a comprehensive view of data sets.
- Integrated with Elastic Maps, Kibana is especially suited for viewing and analyzing geolocation data.
- The software lays strong emphasis on machine learning for detecting errors.
Find Kibana on GitHub here.
Just like Kibana, ease of use is my number one reason for recommending KNIME as one of the best open-source data visualization software out there right now.
The interface is considerably easy to master. It also presents its data output in a way that anyone with basic knowledge of charts and graphs can understand.
Here are other reasons why I think that KNIME is one of the top data visualization tools for you to consider:
- The software leverages artificial intelligence and machine learning a lot. This way, the incidence of data error is significantly reduced, if not eliminated totally.
- Using KNIME, retrieving data from different sources is almost like a stroll in the park.
- It also makes the blending of data fast and easy.
- It is equally easy to take care of the scaling of data and visualizations as your database grows.
KNIME is distributed on the GNU General Public Licence. Find its repository here on GitHub.
Also Read: Bad Data Visualization Examples
ColorBrewer is an open-source cartography web application developed by the Pennsylvania State University geography professor Cynthia Brewer. It is in line with her specialty in regard to visibility and color theory in mapping.
Although ColorBrewer finds its main use in the visualization of geographical data, it is not limited to this. Instead, it is often employed in data visualization generally.
In any case, my main reason for recommending it is its excellent application in geo-data presentation. Here are some of its features and functionalities that I fell for:
- It is free to use.
- It is available online. So, it can be used wherever there is an internet connection.
- Its emphasis on choosing effective color schemes for the presentation of data output makes data visualization spot-on, ensuring greater clarity.
ColorBrewer is distributed on the Apache 2.0 and LGPL 2.1 licenses. Its repository is here on GitHub.
- It can be deployed on any device including mobile devices.
- Although it is a downloadable program, Leaflet is extremely lightweight, occupying only 39 KB of your storage.
- Yet, it sports all the essential features needed for charting and mapping.
- Its features can also be extended through the use of plugins.
- The software’s ease of use is well above average.
- As a developer, I love Leaflet’s well-documented application programming interface and extremely simple source code.
How Do You Evaluate A Data Visualization Tool?
I believe you’ve got this question on your mind all along. So, let’s take a look at it before we wrap up this discussion.
It is one thing to have a list of recommendations (as above), but it is a different thing to know the reason behind the recommendations. If you can have the reasons at your fingertips, then, you can always choose for yourself even if the climate changes (for instance, if a once-great tool begins to fall short of the mark).
So, what are the points that you should consider in choosing a data visualization tool? Below, are the points I have for you at the moment:
1. The tool must agree with your level of expertise
You know, no matter how good a data visualization tool may be if you cannot get the hang of it, it’s going to be valueless to you. So, you need to begin the consideration from yourself. Ask yourself, “do I have what it takes to use this tool?” If your answer is yes, then proceed to use it. Otherwise, you should back out.
Check Out: Misleading Data Visualization Examples
2. Consider the type of data type you are planning to present
Every tool has its focus areas. So, ask yourself if a particular tool you are considering would be the one to meet your demands at the moment.
3. Consider your audience
For instance, if you are presenting geographical data to elementary school kids, you know it has to be really colorful. So, let this consideration guide your choice of visualization tool.
4. Consider the level of expertise of your team members
Yes, if you must work with other people in co-viewing data output, then, you need to look beyond just your own expertise level.
We’ve come a long way in this discussion. Thus far, I believe you want to know what’s my pick of all picks among the items on the list. If we were to discount the particular needs and preferences that anyone may have, I would pick Google Charts above the rest.
What are the reasons behind my choice? Of all the items on the list, Google Charts is the most versatile in terms of output options. So, it is likely to meet the needs of most users.
Also with its easy integration with other Google services like Maps and Sheets, sourcing and integrating data is the easiest with Google Charts among the other options. Moreover, Google Charts is one of the easiest to use and access on the list.
Tom loves to write on technology, e-commerce & internet marketing.
Tom has been a full-time internet marketer for two decades now, earning millions of dollars while living life on his own terms. Along the way, he’s also coached thousands of other people to success.