nba-monte-carlo VS Blazer

We have support for duckdb (and CSVs and Parquet through duckdb). We don't support python, but some people have also told us they have used evidence as the front-end for a python project - used python to do data transformation and calculations, then dumped the results into a duckdb file in an evidence project and built the visuals and narrative in evidence.

"Containerized" approaches with evidence are also quite interesting - lets you combine several tools and use evidence as the last mile. Here's a great example: https://github.com/matsonj/nba-monte-carlo

I try to avoid these tools wherever possible, given the choice I'd always go for tools like Blazer.

https://github.com/ankane/blazer

No such luck in my current role, Looker and PowerBI are both in use by different bits of the org and nobody has the ability to delve into the underlying figures.

As u/jaxn said you could use Blazer for this kind of thing. I would also look into materialized views or custom tables and a scheduled job that calculates the metrics they care about. That will take you a long way. Eventually you can use something like Metabase but I would put that off for as long as possible as it's really expensive and pretty involved.

And it's Open Source: https://github.com/evidence-dev/evidence

I'd also highly recommend Blazer https://github.com/ankane/blazer if you are into the Ruby on Rails world. It's super solid, and it's been an indispensable tool integrated to all my projects.

I use Ahoy too, but I don't have very good visibility into the data. I should spend more time building queries and creating charts. I should probably set up blazer as well: https://github.com/ankane/blazer

blazer

The Blazer gem provides a nice way to analyze the results easily. It is simple to install and allows SQL queries to run against tables. The query here shows that the candidate implementation is significantly faster than the original.