superset VS grouparoo

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.

Reverse ETL is on AirByte's roadmap under the "Future / Not prioritized" section. I wanted to use Grouparoo as a short term solution, but the repo was archived and I think they stopped taking new cloud customers (unknown if this is wrong/outdated).

There are other tools that we will have to adopt in the future but haven’t yet due to lack of necessity. Specifically, one category that is popular in modern data stacks is Reverse ETL (Hightouch, Census, or Grouparoo). We currently don’t have a usecase for piping data back into 3rd party tools but it will definitely come up in the future.

Reverse ETL: Grouparoo, Castled

Grouparoo, the open source Reverse ETL tool we are building, does all of these things. https://www.grouparoo.com

Ingestion / ETL: Airbyte, Singer, Jitsu Transformation: dbt Orchestration: Airflow, Dagster Testing: GreatExpectations Observability: Monosi Reverse ETL: Grouparoo, Castled Visualization: Lightdash, Superset

Grouparoo | Remote (US) | Remote-OK | https://www.grouparoo.com

Grouparoo is a venture-backed software company building open source data tools that make data reliable, accessible, and actionable. We’re empowering teams to make great customer experiences, driven by data. While engineering teams have gotten good at storing and generating data about their customers, it’s rare that this data is used to its full potential in external applications. Grouparoo makes these integrations easy by providing a framework for defining your customer data and reliably syncing it to external tools.

To learn more about who we are, our engineering culture, and whether this is the right place for you, read our Key Values profile: https://www.keyvalues.com/grouparoo

Here are our open roles:

- Senior Backend / Lead Engineer: https://jobs.lever.co/grouparoo/6ba485d1-a5a4-41f0-9fa5-920a...

- Developer Advocate: https://jobs.lever.co/grouparoo/5e1531b4-7ec8-4c10-8e52-fc23...

Tech Stack: TypeScript / Javascript / Node.js, ActionHero, React + Next.js, Postgres & Redis, and whole lot of third-party APIs!

Congrats on the launch! Hightouch looks great and this need is real. Things seem to be going well, so I don't think I'm taking too much away by mentioning that we have been been working on Grouparoo, an open source alternative that solves similar pain points.

A few differences: git developer workflow focused (branches, CI, PRs, etc), ability to self host, segmentation in destinations (tagging people in mailchimp based on rules, for example)

https://www.grouparoo.com

We are building Grouparoo. Obviously, it's a biased sample but we are seeing a few trends at play.

Has anyone heard or used of reverse etl or Hightouch or open sourced Grouparoo?