lowdefy VS superset

I'm really enjoying reading through the docs and the tutorial. We've created Lowdefy, a config web-stack which makes it really simple to build quite advanced web apps. We're writing everything in YAML, but it has it's limitations, specifically when doing config type checking and IDE extensions that go beyond just YAML.

I've been looking for a way to have typed objects in the config to do config suggestions and type checking.. PKL looks like it can do this for us. And with the JSON output we might even be able to get there with minimal effort.

Is there anyone here with some PKL experience that would be willing to answer some technical questions re the use of PKL for more advanced, nested config?

See Lowdefy:

https://lowdefy.com/

https://github.com/lowdefy/lowdefy

Awsome! With Lowdefy we tried to build a low-code framework that works like code. We’ve developed a schema in which to define applications and we’ve built all kinds of apps for enterprise customers. Massive, advanced CRM systems, call centre solutions, ticketing systems, a light MRP, all kinds of survey apps and so many dashboards. Even our docs and our website are Lowdefy apps!

Give Lowdefy a try and reach out it you have any questions or want to see what is possible :) (We need to invest a lot more into content and examples, bootstapping is a grind!)

https://github.com/lowdefy/lowdefy

Also add Lowdefy onto the list https://github.com/lowdefy/lowdefy

co-founder here :)

100% this. And yes, good documentation takes a lot of investment but it pays off like compound interest. But with that done, it becomes even more important not to pull the carpet for no good reason, you are building a tower and documentation is at the foundation.

We’ve built Lowdefy [1] as an open source project and documented it with all effort, 200 pages of docs. I often forget why or how something works and then jump to the docs. This investment keeps on paying of as we use Lowdefy to build customer apps, new devs in the team typically take less than two week to get up to speed and start making contributions, the sharp ones, just a two or three days.

This year, we’re extended our documentation onto customer apps aswell, with flow diagrams, state machine definitions, detailed field level explication schema definitions, and end user test procedures. The key here for this documentation is detail. It should be easier to reach for the docs and the the answer, than to dive in the code and interpret it.

1 - https://github.com/lowdefy/lowdefy

https://github.com/lowdefy/lowdefy Co-Founder here.

Check out https://lowdefy.com/ they even have a sample survey app as one of their examples.

You can build an app that would do this

Lowdefy is a low code tool that uses AG Grid as a block component, allowing you to create apps which render data in AG Grid without a lot of coding knowledge. There is a Lowdefy example using AG Grid here.

Shameless plug, also check out Lowdefy - https://github.com/lowdefy/lowdefy

I’m also going to jump in here and say try Lowdefy https://github.com/lowdefy/lowdefy - co-founder here.

We take a different angle and believe that low code should still work like code. We focus on a developer first approach.

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.