I think those points are on the mark. I added some notes to the similar question that asked for a comparison of dbt, cube and MetricFlow!
I'll just say Joins are hard, but if solved elegantly they allow you to do some really interesting stuff. As an example in MetricFlow you can traverse the data graph in your queries. For example if you go `pip install metricflow` and try the tutorial: `mf_tutorial`
You can see what I mean by running queries that traverse the multiple joins to get to other dimensions:
1. Ask for transactions by day by customer `mf query --metrics transactions --dimensions ds,customer --order ds`
2. Ask for transactions by day by customer country `mf query --metrics transactions --dimensions ds,customer__country --order ds`
3. Ask for transactions by day by customer region where we traverse through a country to region mapping
`mf query --metrics transactions --dimensions ds,customer__country__region --order ds`
I'll just say Joins are hard, but if solved elegantly they allow you to do some really interesting stuff. As an example in MetricFlow you can traverse the data graph in your queries. For example if you go `pip install metricflow` and try the tutorial: `mf_tutorial`
You can see what I mean by running queries that traverse the multiple joins to get to other dimensions:
1. Ask for transactions by day by customer `mf query --metrics transactions --dimensions ds,customer --order ds`
2. Ask for transactions by day by customer country `mf query --metrics transactions --dimensions ds,customer__country --order ds`
3. Ask for transactions by day by customer region where we traverse through a country to region mapping `mf query --metrics transactions --dimensions ds,customer__country__region --order ds`