1) Go beyond SQL. This is not a big deal for transactional databases, where most of the logic is well-expressible in standard SQL. But analytics are another story since there is so much custom logic (how do you implement a data mining algorithm, like association rules, in SQL? It's not easy!)
2) Go parallel. Nobody knew what a good parallel API looked like before Google brought MapReduce and proved its value by using its own systems as guinea pigs. Since our Data Warehouse architecture is natively MPP, MapReduce is a great fit to speed up analytical applications.
The combination of these two possibilities we believe can be revolutionary for Data Warehousing. If you're interested to read more take a look at our blog.