Motor Admin VS superset

Tangential: MotorAdmin is a pretty reasonable, mountable, Rails-based option.

I'm using it to pack BrowserUp, a command line app that can run load tests in Ruby using your own Ruby/Capybara/Selenium/Cuprite libraries (note: still alpha, ping if interested). Motor Admin ships a web app with a no-code admin utility. I think Shopify would have been way better off using this for their latest command line interface rather than taking on a Node dependency, but they might not have realized this was an option, so that's part of why I'm sharing this.

There seems enough competitors when this looks like a tool from 20 years ago with limited functionality.

If you somehow do not use the obvious GUI tools like TablePlus, Postico or SequelAce but prefer web based, there are some that actually look modern.

https://www.getmotoradmin.com/

https://www.nocodb.com/

https://redash.io/

For readonly usage, Metabase is good.

You should check Motor Admin open-source tool (disclaimer: I'm the creator of the tool): https://github.com/motor-admin/motor-admin

>Auto-generating the GUI from SQL schema, similar to the original Django Admin

Thats exactly how Motor Admin works :)

The link posted here is to a Supabase-specific landing page.

Their main home page doesn't mention Supabase: https://www.getmotoradmin.com/

If you want a more codeless approach (not as customizable, but it already looks good and works fine out of the box), I suggest Motor Admin. You just plop it in your project, run a few migrations and it's ready to go. From that point forward, you can add forms, graphs, and query all of your models without coding as much.

Github

There are many open-source no-code tools so you can gain full control over your data/application with them as well as contribute into the source code.

For instance, you can deploy an admin panel with https://www.getmotoradmin.com/ (plug) and save a lot of time building custom internal tools.

Superset is absolutely phenomenal. I really hope Microsoft eventually releases all of their customizations they made to it internally to the OS community someday.

https://www.youtube.com/watch?v=RY0SSvSUkMA

https://github.com/apache/superset/discussions/20094

I recently ran a little shootout between Superset, Metabase, and Lightdash. All have nontrivial weaknesses but I ended up picking Lightdash.

Superset the best of them at _data visualization_ but I honestly found it almost useless for self-serve _BI_ by business users. This issue on how to do joins in Superset (with stalebot making a mess XD) is everything difficult about Superset for BI in a nutshell. https://github.com/apache/superset/issues/8645

Metabase is pretty great and it's definitely the right choice for a startup looking to get low cost BI set up. It still has a very table centric view, but feels built for _BI_ rather than visualization alone.

Lightdash has significant warts (YAML, pivoting being done in the frontend, no symmetric aggregates) but the Looker inspiration is obvious and it makes it easy to present _groups of tables_ to business users ready to rock. I liked Looker before Google acquired it. My business users are comfortable with star and snowflake schemas (not that they know those words) and it was easy to drop Lightdash on top of our existing data warehouse.

Or like not being able to delete a user without running some SQL:

https://github.com/apache/superset/issues/13345

Almostl instantly run into this issue setting up a test instance of Superset. And the issue has been around for years.

Are you interested in trying out Superset, but you're intimidated by the local setup process? Worry not! Superset needs some initial setup to install locally, but I've got a streamlined way to get started - using the makefile! This file contains a set of scripts to simplify the setup process.

Recently I discovered BigQuery public datasets - just over 200 datasets available for directly querying via SQL. I think this is a great thing! I can connect these direct to an analytics platform (we use Apache Superset which uses Python SQLAlchemy under the hood) for example and just start dashboarding.

if they don't want to pay for powerbi, can try something like https://superset.apache.org/

In today's fast-paced data-driven world, organizations must analyze data in real-time to make timely and informed decisions. Real-time data analytics enables businesses to gain valuable insights, respond to real-time events, and stay ahead of the competition. Also, the analytics engine must be capable of running analytical queries and returning results in real-time. In this article, we will explore how you can build a real-time data analytics solution using the open-source tools Redpanda a distributed streaming platform, Apache Superset, a data visualization, and a business intelligence platform, combined with RisingWave a streaming database.