Should i use a star schema or a single wide table for our sales dashboard?

[Justin_L]

I’m trying to decide between using a star schema or a single wide table for our new sales dashboard, and I’m stuck. The star schema feels right for joining dimensions cleanly, but I keep wondering if the performance hit from all those joins will make the dashboard too slow for our users.

[JonathanWW]

We went with a star schema for the dashboard project last year. After we added materialized views for the most common joins and precomputed date and geography rollups, the drill downs stayed responsive. We kept the fact table lean and let the dimensions do the heavy lifting; most users never hit more than a couple of lookups. It felt slow only when the cache expired and the BI tool re-ran every tiny filter.

[GeorgeA]

We tried the single wide table approach for a pilot. Early on performance was great, but as soon as the product dimension grew or users drilled into subcategories, things stalled. ETL grew brittle too, and adding a new attribute meant touching many reports.

[SamuelBR]

I keep thinking there might be a middle ground, like pre-aggregates or a hybrid approach, but I never got a clear signal that it would solve the real bottleneck. Sometimes the lag is in the browser rendering more than in the data retrieval, which makes it feel like the model isn't the root cause.

[AbigailJ]

If you have any data on typical filter combos, you could test with and without pre-aggregation and compare times. How big will your production data be?