xsevensinzx (8/25/2016)

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MMartin1 (8/25/2016)

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I think overweighting the size portion is not taking into consideration the many benefits of the denormalised model when it comes to reporting, and can gravitate ones thinking away from the positives. Warehouses will/should be big. 100,000 rows or 200,000 rows for that matter is no real concern. Remember the purpose of a warehouse is to retain well organized history. A dimensional table that will probably be in the MBs of storage space will pale in comparison to the GBs of size of a fact table.

It can really depend on the use case. You shouldn't assume that big means you should not consider the impact of large dimensions for reporting. Using my warehouse in example, I have facts that have half a billion if not a billion in records. Reports can span a good portion of those records due to the nature of the questions that are being asked of the warehouse. When you add large denormalized dimensions into the mix, you have to consider the perfomance implications.

If you're just supporting canned reporting where the users are looking at slices of data in weekly, monthly or quarterly rollups, then the performance implications is not as wide. Yet, you toss in 1,000 users hitting the same tables for the same report, things may change.