What’s the best choice: star schema or snowflake for our data warehouse?
#1
My sister was recently diagnosed with celiac disease and I want to make sure she doesn't feel left out during family meals. What are your favorite gluten-free recipes that actually taste good?

I'm looking for everything from main dishes to dessert recipes that work well gluten-free. I've tried some store-bought gluten-free products and they're... not great. What are your best gluten-free recipes that you'd serve to anyone, regardless of dietary restrictions?
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#2
I’m trying to decide between a star and snowflake schema for our new sales data warehouse, and I keep going in circles. My team wants the simplicity of the star schema for faster queries, but our product dimension has multiple slowly changing hierarchies that almost push us toward a snowflake design. I’m worried about the performance hit from more joins, but also about the maintenance complexity if we flatten everything. Has anyone actually been in this spot and regretted their choice later?
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#3
Yeah, we went with a star design and learned the hard way that the product dimension with slow changing hierarchies kept forcing extra joins.
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#4
The speed boost from fewer joins was real for simple queries, but the moment someone asked for a deep drill into the product lineage, the delays climbed fast.
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#5
We ended up flattening a lot of it and then cursed the maintenance when the product lines changed and there were 5 versions of a dimension.
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#6
One colleague argued a hybrid approach, keeping a slim fact and using a few conformed dimensions to avoid a ton of joins, and it helped, but not perfectly.
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#7
Maybe the real problem is not the schema shape but how we manage slowly changing attributes in the product area; if the data team can't keep the history clean, everything else feels like lipstick on a pig.
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#8
Are we sure the issue is the hierarchy design and not the ETL schedule, indexing, or the reporting layer causing the friction?
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