Countly VS superset

* [Countly](https://countly.com/)

I suggest you use Countly, they have a free edition (Community) also its Self-hosted.

Happy cake day. Yeah, nowadays there are so many analytics variants out there: Swetrix, Plausible, offen, count.ly any many more.

Hello HN, founder of Countly (https://count.ly) here. As you might know, we are the creators of one of the first open-source product analytics platforms that has 10+ SDKs for mobile, desktop and web applications. We've been working on a new SaaS, myCountly, to help you launch your own Countly servers in any location, so your user data stays close to home.

We are going to do an alpha launch soon, and looking for alpha testers, please signup below if interested:

https://gocountly.typeform.com/to/ayGA2EOm

Some highlights of myCountly:

- Your dedicated, fully managed deployment(s), not a shared service

Is countly still operational? Can't connect to their website https://count.ly/

Countly (https://github.com/Countly/countly-server)

Always surprised more people don’t use countly. Runs nice in docker or digital ocean. https://count.ly. Been self hosting it for years with few issues.

Countly (website, GitHub) is also an open-source product analytics platform that is designed primarily for marketing organizations. It helps marketers track website information (website transactions, campaigns, and sources that led visitors to the website, etc.). Countly also collects real-time mobile analytics metrics like active users, time spent in-app, customer location, etc., in a unified view on your dashboard.

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.