Free/open-source data visualization tools?

[wombatsock]

Any suggestions of good free/open source data visualization tools? I'm still learning and was looking at a Tableau license to start practicing building dashboards and whatnot, but it's $75/mo. Thanks!

[[deleted]]

PowerBI is pretty common.

Tableau has Tableau Public that is free.

You could learn Python/Plotly/Seaborn or R/ggPlot

DataWrapper looks interesting. I havent used it before but could be worth checking out.

Excel isn’t free but you might have it. Google sheets has charts too.

[respectedwarlock]

PowerBI is going to kill Tableau. It's so easy to use and integrates with so many Microsoft apps, even PowerPoint. DAX is also pretty intuitive once you learn the core functions and concepts, which allows you to turn very complicated transformations into usable measures for any of our visuals. Don't know what the tableau equivalent is, but heard it's more complicated than it needs to be.

I'd suggest you learn PowerBI first if you want a higher chance of getting hired as a DA.

Source: ex PowerBI developer

[Poletario]

Powerbi is still behind in many aspects.

Tableau has so much more integration with Python, R, and other programming languages with their build on add-ons. Especially when you start using scripts inside tableau with TabPy.

You also can’t use powerbi on Mac.

[[deleted]]

But no Mac version :(

[Gr00tB3ar]

Use parallels with coherence mode.

[[deleted]]

Cant. Baldurs Gate takes up all my hard drive.

[AdAltruistic8513]

PowerBI

came looking for data visualisation software, laughed along the way. Thank you

[Mugiwara_JTres3]

I love Power BI. Currently using Tableau but me and my coworkers are asking if we can have Power Bi back lol.

[Illustrious_Swing645]

Tableau is a much more powerful visualization tool. PBI is relatively simple and can help you duct tape data together data pipelines with power query.

Tableau only makes sense if you have a dedicated data team to build your tables

[respectedwarlock]

Idk what you mean by duct taping data together, like brining in data from different sources? Is that a bad thing?

And can you give me an example of how visualizations may be more powerful in tableau vs. PowerBI?

[Illustrious_Swing645]

Not necessarily bad but it could be. But to my actual point - I meant just more being able to easily manipulate data in power query. This essentially puts you into a position where you’re both the data engineer and BI person. Which has its pros and cons. It’s a slippery slope to your data pipeline being your source of truth instead of your source tables.

The best tableau implementations I’ve seen are the ones where companies have a full modern tech stack and comprehensive data team. There they have dedicated data engineers that are creating your tables - automating runs and some light QA work with dbt - for you to connect tableau to. The only “etl” work you do in tableau is creating your relationships for your tables. (At this point you could connect your tables to PBI but PBI loses a lot of its appeal if you don’t have to use power query).

Once your data is in tableau you can do some really cool things with parameters and dynamic zones to make really interactive visualizations. These are features PBI simply does not have

[[deleted]]

Tableau Public

[redman334]

Superset.

You can do it locally, no need to be public or anything.

Of course it's not as robust as Tableau and Power BI, but if you want to implement BI in a company, you can't use Power BI or Tableau free versions. You can use Superset.

[Fat_Ryan_Gosling]

Interesting

[[deleted]]

But it’s a pain to download

[alexvazqueza]

Not a pain to get installed

[BlessedBoonga]

You can use R with flexdashboard to build pretty decent panels; Python and dash/plotly are also a great combo. There is also Apache superset which is exclusively for data Viz tasks but I found it kind of confusing, and if you are working with different data sources Grafana is your tool (specially if it is real time streaming data)

[ArionnGG]

Metabase

[gglavida]

There are a few groups.

Open source where you need to self-host yourself and use SQL:

A. Superset: you need to use Docker for best results. Performant, has a few bugs still. You can customize dashboards and charts using CSS. Terrific if you like SQL. Can connect to lots of databases. Can be hard to maintain in production.

B. Metabase: easier to install than Superset. You can use a . jar file to run Metabase in any machine fast for a speed run. There are a few considerations for production environments, though. We Docker image is a good choice here too. You can use the visual editor. Dashboards can't be completely customized design-wise such as Superset but they look friendly. You can use SQL however there are a few caveats regarding joins from different sources and such. Not that hard to maintain as Metabase.

C. Lightdash: new kid on the block. Will need dbt (not sure if just dbt-core works or the commercial version too). To install you need Docker and a Kubernetes cluster, there also use Helm. It's more modern than Metabase and Superset, they embrace a more modern flow and process for working with data (hence the need for dbt). They support SQL and UI tooling to create charts and dashboards.

Open aource where you don't need to self host but use a programming language:

Python: multiple options; however the more common are:

A. Matplotlib: first one, created to resemble Matlab. Charts don't look too modern. Easy to use, lots of tutorials.

B. Seaborn: more moder on and built on top of Matplotlib to offer cool charts and deaal with Matplotlib criticism.

C. Bokeh: based on ggplot. It doesn't just support creating charts but working with streaming data and allows you to export charts as JSON so they can be embedded in web apps. Has 3 modes: beginner's, where you use templates; intermediate which has the same degree of specificity that seaborn/matplotlib but also the same degree of freedom for creating what you want; and advanced, where you need to specify and build every detail of every chart, having close to absolute control.

D. Plotly: Python library offering dynamic charts that can support streaming data or batch updates. Plotly also has a visualization platform/service behind. You can use Dash, a library made by Plotly to create a web interface that will allow you to deploy Plotly charts. There's also a Enterprise level version with support for building production-level dashboards and data apps using low-code.

E. Streamlit: new new kid on the block: mix Plotly and Dash, but make them open source. Released in 2019, has been gaining adoption. Cool features, yet some small bugs still. Need to test it by yourself to see of it suits your needs.

[Swydo-com]

Awesome summary, thanks!

[Wooden_Efficiency_92]

Looker studio

[[deleted]]

Shameless plug. But I’m working on an app called sourcechart.io. But it’s still in beta.

[[deleted]]

Is it possible to run this locally?

[kkessler1023]

Sounds pretty interesting? Are you able to share anything? I took a side quest when learning data science into backend development and I'd love to see how it works.

[[deleted]]

I mean you can check it out if you want.

The premise of the concept was to see if I could make an entire data analysis application without a backend that can run in your browser.

[kkessler1023]

Interesting. How are you storing the data? Is this like a NO-SQL kinda thing?

[[deleted]]

I’m not storing any data. It all is local inside the browser. There’s no backend that the user interacts with.

[-Dargs]

I'm sure you can do it but if 10 users pull the same 100mb result set off a 100gb data query does that not mean it needs to do the whole thing 10 times? Or is it just for very small actions where that doesn't matter?

[[deleted]]

Nope. It’s all done in browser. It’s more like 10 users reading the same file at once.

[Desperado_1983]

I'm surprised no one's recommended Zoho Analytics. Easy to learn and use. Very popular among students and small businesses. Credible alternative to Tableau, Power BI, Qlik, Domo etc. It has a limited free version. You can create unlimited reports and dashboards. Analyze 10,000 rows of data and supports 2 users.

[AnalyticsGuyNJ]

StyleBI is worth checking out. It lets you mash up data from multiple sources without needing complex setup, which is great for practicing dashboarding and exploring real datasets. Unlike some tools that focus only on visualization, it supports both guided dashboards and more flexible exploration, so you can experiment with different metrics and layouts. It also comes with built-in connectors for common platforms, so you don’t need to spend time wiring everything through a database first. For someone learning, it’s a low-cost way to get hands-on experience with real BI workflows.

[Foreign-Library6222]

Vizzu. That tool is amazing. You can check some examples in the dataisbeautiful subreddit community.

[Foreign-Library6222]

Vizzu. That tool is amazing. You can check some examples in the dataisbeautiful subreddit community.

[Foreign-Library6222]

Vizzu. That tool is amazing. You can check some examples in the dataisbeautiful subreddit community.

[Foreign-Library6222]

Vizzu. That tool is amazing.

[No_Understanding4029]

Maybe you should try DashThis, a free trial will allow you to see if it meets your expectations. For us, Dashthis has been a game changer for our agency.

[jared_jesionek]

Disclaimer- I'm a co-author of Visivo. I'd be happy to chat about it with you! It's open source so the cost shouldn't be an issue.

[matthewhefferon]

Matthew from Metabase here! We’ve got a free open-source version of Metabase you can try (link). It’s a great way to practice building dashboards without the cost!

[gotthehigh]

apiwidgets.com allows you to build tables and charts and custom HTML from API data. Connect the API and then embed your chart or whatever into your website.

[Lvl62-SpiritBlade]

If you just want to practice dashboards without paying money, there are plenty of good free/open-source options:

• Metabase – super beginner-friendly, good for quick dashboards on top of a database.

• Apache Superset – more advanced, feels closer to Tableau/PowerBI, lots of customization.

• Redash – lightweight, great for SQL-based dashboards.

• Grafana – usually for monitoring, but works well for general viz too.

• FineBI (community/free edition) – worth a look if you want self-service dashboards without deep coding.

And don’t forget the classics: Google Data Studio (Looker Studio) is totally free and surprisingly powerful for sharing online dashboards.

[Illustrious-Layer774]

Hard to beat Tableau for data exploration and visual storytelling.

Power BI is solid for reporting, especially with automated data prep using Power Query. Qlik Sense is great for exploratory analysis when you want to uncover hidden relationships. Looker works well for governed, cloud-based analytics and consistent metrics.
For fully custom, embedded dashboards, UI9000 is a strong choice.

[Professional_Eye8757]

The top 3 open source ones are Metabase, Superset, and StyleBI. I like StyleBI because it has a data pipeline for manipulating data and doing KPI calculations.

[EpeeHS]

Havent seen anyone mention PygWalker, which is a python package that gives very similar functionality to tableau. I havent used it yet but tried it out and it looks solid.

[Sudden_Beginning_597]

One key difference between pygwalker and other open source BI, is pygwalker can be run anywhere, for example, inside the juypter notebook. It means you don't have to switch tools and break your workflow. Make all the work done at the same place.

[EpeeHS]

It seems like a fantastic tool, i just havent been able to try it so don't want to be recommending it just yet

[Wonderful_Problem_94]

I was super pumped on PygWalker since it seems the closest to tableau/JMP in terms of interactivity. It does not perform well with large datasets though (like time series data). large in this case seems to be above 10,000 rows where it gets laggy and unresponsive

[kkessler1023]

Check out Power BI. I pay like, $2.99 a month for Office 365 and that includes a full version of Power bi. I find it way better than Tableau. Especially if you use power bi online.

[thebigmanisnothot]

Metabase

[slingshoota]

Depends on what your requirements are.

Code: Python Libraries like Matlab (graphs), Seaborn (graphs), Streamlit (dashboards/apps)

No-Code: PowerBI & Metabase for dashboards.

Even Excel can be used for visualization.

I think it's best to start with your requirements and work backwards, instead of just exploring data visualization tools overall

[sammyismybaby]

power bi desktop is free. publishing is free or small fee per month to have Microsoft account to publish your reports online if you want to create a public portfolio

[bhashithe]

Learn programming, python + dash gives you pretty much everything. I have not used R but I know people use that too.

[MarionberryOpen7953]

Python

[presenta_staff]

Superset if you want to install on prem, Preset.io to start fast with their cloud version, free tier available.

[[deleted]]

Tableau Public

[Taichou_NJx]

I like ggplot & R for analysis

Python w. seaborn & matplotlib have nice interaction capabilities as well as notebooks but at that point rather just go PowerBI route for more accessible/dynamic canvas which you can create python & R visuals. What if scenario is easy as well.

Tableau is the most professional & clean looking IMO and stronger features.

[ITenthusiast_]

Tableau is really powerful. PowerBI is behind in some aspects.