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. - Source: Hacker News / 3 months ago

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. Source: 11 months ago

And it's Open Source: https://github.com/evidence-dev/evidence 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. - Source: Hacker News / about 1 year ago

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. - Source: Hacker News / almost 2 years ago

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. - Source: dev.to / about 2 years ago

Example notebooks are included in the repo and can be previewed using nbviewer:. Source: over 1 year ago

Nbviewer (https://nbviewer.org/): very easy to use for smaller jupyter notebook that does not require heavy rendering. Source: over 1 year ago

Nbconvert renders everything exactly as it looks in your notebook app into a read-only HTML version and is what GitHub uses for notebooks. Interactive plots from Bokeh, Holoviews, etc can still work if you trust the JS, and since editing notebooks while showing them during a meeting usually doesn't go well, read-only is probably good enough (eager to hear feedback on this point though). The nice thing is that... Source: over 1 year ago

Just as a heads up, I used plotly to generate a lot of the charts, so you'll need to view it from an nbviewer like nbviewer.org. Source: about 2 years ago

I used a lot of plotly not knowing that Github wouldn't show it, so you'll need notebook viewer like nbviewer.org to see some of the charts. Source: about 2 years ago