rudder-sdk-unity VS superset

You may recall a blog post in 2020 where we discussed how Torpedo was leveraging the RudderStack Unity SDK to send over 1 billion events per month to Redshift to power a robust analytics dashboard built with SuperSet. This cost-effective solution shed light on what player activities were important for driving engagement and helped refine the definition of high-value player cohorts as well as identify player churn. After crossing the 100 million download milestone, the teams at Wynn Resorts and Torpedo were looking for innovative ways to continue improving the gaming experience, user engagement and most importantly, reduce the churn on those high-value customers making in-app purchases.

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.