Dr. Mario's 2nd 🧠

Data Modelling for Startups

2 min read

rw-book-cover

Metadata

Highlights

chasm trap’. It looks like this: If we want to compare two fact tables, we have to work out how to ‘cross the chasm’ (i.e. understand the relationship between the two based on their shared dimensions and join them appropriately). The reason it’s a ‘trap’ is because when you have multiple ‘join paths’ between fact tables, it’s very easy to end up double counting facts. Some business intelligence software can manage this issue. Things can get a bit more dangerous when we create a ‘galaxy’ schema — a set of star schemas connected via conformed (shared) dimensions: An example ‘galaxy schema’. Note that queries including the ‘Subscription Creation’ fact could reference the ‘Subscriptions’ dimension, which is NOT shared with the other fact tables. This can sometimes cause problems if queries regarding multiple different facts reference dimensions that are not shared. (View Highlight)

One way to avoid this is to find a way (e.g. in a business intelligence tool) to expose coherent groups of data models that avoid such pitfalls. These model groups would contain either • a single star • a single chasm (View Highlight)

OBT - One Big Table

Because we used RIGHT JOIN to construct our OBT model, some rows have dimension attributes, but NULL sales measures. We can use the query above to reveal which products have zero sales value associated with them (View Highlight)

Graph View