Open source Business intelligence platform made with Python
superset.apache.org
superset.apache.org
I'm also looking for suggestions. A client of ours has a classic CSV representing sales data. I want to build a dashboard to make the visualization of the dataset easier for the client. I was looking both for Metabase and Superset. However, I don't really know if there are any other products out there which could help in this. I'm obviously referring to open source products which could be easily deployed, so I'm removing tools like Tableau or PowerBI from the list.
Edit: Metabase probably a fair alternative if they are likely to need the number of queries and views to increase, and even more so if you'd like to have them "self serve"
I definitely would not consider Superset unless you know that you need the advanced features.
Here's a CSV example https://bl.ocks.org/texodus/02d8fd10aef21b19d6165cf92e43e668 Here's a dashboard example (with a lot of data) https://texodus.github.io/nypd-ccrb/
The expectation is that data is loaded into your database, which is reasonable. None of these tools are built to ingest data from multiple disparate sources and perform their own aggregations (like Tableau), but rather depend on locality of data on the database under query.
[1] https://github.com/Markenson/csv-metabase-driver/releases/ta...
In both cases the audience for most traditional notebooks are data scientists. In contrast my target audience is backend developers and hands-on engineering managers who want to build operational and business dashboards and recurring email exports by combining data from multiple different data sources.
So it comes built in with setup for every major database. It can be run as a desktop app which makes it easier to get running than web-based notebooks, or as a web app where you can make dashboards and recurring email exports. And eventually my goal is to add high level connections to common APIs developers/managers use like Github, JIRA, Kubernetes controllers, etc. so you can build reports more easily across your services.
Also, the notebook interface on its own has felt to me like it doesn't treat querying databases as a first class thing. With DataStation the database query UI is separate from the programming UI. And like a SQL gui there's builtin support for specifying (encrypted) credentials to your various databases and builtin support for querying them over SSH proxies.
In contrast though Zeppelin and Jupyter are certainly much more mature and extensible.
In my opinion, it needs Ui polish and direction, as 9times out of 10, the initial page of a visualization is an error because the default visualization doesn’t work with the selected table without some config. This is really confusing for users who are new to it and just want to graph some stuff.
could you elaborate on this?
Metabase is a great product. Simple to deploy and use, stable, easy for non techies and SQL analyst types. It tends to be my go-to.
Superset is a great product and we are lucky to have this quality of BI tools for free. It does however have a few extra rough edges and quirks where your query doesn't render as you would expect, especially with niche databases such as Druid and Clickhouse (via a third party driver). I often find myself checking log files to see what actual SQL was issued from the front-end. It's also slightly more intimidating for end users too.
Metabase is easier to deploy. I use a single binary vs (I think) a docker compose setup for Superset.
Both have a cloud offering, Preset.io is the Superset one which we are just using now and having a productive time with.
I would look at both Metabase and Superset before the heavier weight commercial options if the choice was purely down to product features. Tableau, Looker et al don't seem to be bring much to the table above these, and they are both quite difficult and expensive commercial organisations to deal with.
With all of this said, building simple reports and dashboards over aggregated data is a fairly commodity task nowadays. Unless you are doing anything funky, all of these tools and tens of others will do the job.
These extend to the user interface and ux. Unless you need the more sophisticated analytics features of Superset then I'd definitely start with Metabase.
While Metabase is not the most sophisticated tool out there, we had great experience onboarding users that only knew Excel before. Which resulted in them answering the majority of basic questions about data themselves, reducing the load on the BI/data staff.
PowerBi pricing($10 per seat) is so nice compared to Tableau($85 per seat last I checked).
Then I realized that Metabase and Superset both can be run on promises free of monthly costs. That can be huge when the number of users fluctuates greatly.
Superset and Redash are great for analysts and technical users looking over more control of the output, with no expectation that the end user consuming the data will want to "drill down" further or satiate their own curiosity. But you can pull things off with these tools that Metabase isn't built for.
Yeah, it's a little rough to get going, but once it is, we've found it to be a really powerful (and actively developed!) BI tool. It's even better with dbt + MetriQL [0], which can automatically sync Superset's dataset metadata directly with properties you set up in dbt.
Adding custom visualizations is much harder than it should be, but they're very much aware of that, and working to address it. Their Slack community is super-helpful, too.
[0]: https://metriql.com
Dumb question - what makes this project better / worse than competing BI / data viz tools like Jupyter notebook, Tableau, and MS Power BI with Excel?
Notebook based analysis is more programmatic. I could for instance pull in a query, apply statistical functions, build an ML model, perform logic, call APIs etc.
I think both have a place in many organisations, but the programmatic side is where there is more potential. Yet another dashboard is a bit uninspiring nowadays.
No business user is going to write notebook code, on the other hand I've seen some great exploration, analysis, and dashboarding work done by business users with the right self-serve setup in Looker. dbt + Metabase or Superset seems like a great cheaper / more open alternative stack.
From what I've seen, ML and programmatic stuff is flashy, but enabling business users to easily get tables and bar charts is how you get everyone making more sound decisions.
I came away with the impression that BI is simply not accessible outside of enterprise level orgs. So I love seeing tools like this come into the market. You should also check out https://cube.dev - they're much less out-of-the-box, but super flexible for developers.
Metabase has the most beautiful and intuitive UI among the three, and works well when your use case is supported, but further customization is difficult. Superset is good for customization, but for setting up some standard chart types/dashboards the effort will be more than Metabase. Redash is sort of in between, and sometimes feel slow.
Ofc, it's not good to scale, but that doesn't matter --- it does the job well at small scales.
[1] https://superset.apache.org/docs/databases/google-sheets
Is there more of them?
It is mostly written in C++ and Python, integrates external libraries like LLVM, OpenCL, CUDA, etc.