Comparison
One big tablevsStar schema
One big table
every attribute anyone might filter on is pre-joined onto the fact, so the analyst's query has no joins in it at all.
Flattening a star into a single wide table so that consumers never join anything. On columnar storage the unused columns cost nothing to read, so the classic objection largely evaporates, and BI tools and self-service users are dramatically less likely to get a wrong answer. What you give up is history and reuse: a change to a dimension attribute means rebuilding the whole table, and every consumer is coupled to one team's idea of which columns matter.
Full entry →Star schema
every dimension is one join away from the fact, so no query needs more than a single hop to get a label.
A central fact table surrounded by fully denormalised dimensions, each reachable in one join. It is the default shape for analytical modelling because it is predictable to write, cheap to plan and easy for a BI tool to reason about. The redundancy inside the dimensions is the deliberate trade — you accept repeated country names in exchange for never chaining joins in a report.
Full entry →