Yes, but you need understand what you want from your data warehouse. If you need to ingest largely structured data and run SQL-type queries against it this is the core Hive use case and is probably what most people think when considering a DW and Hadoop.
If you need more custom analytics then its possible but at that point your integration with the front-end, user-facing querry tools will also become more specialised.
I also recommend thinking about what things Hadoop is good for and what workloads may make sense in a traditional DW. At my current company I've got both and I see it as case of complimentary and not necessary competing technologies.
In either case remember that Hadoop has a core trade-off of optimizing for throughput at the cost of latency. In a true DW type scenario this shouldn't be a consideration as almost by definition no one expects DW queries to have sub-second response times.
What is really nice is that with Hadoop you can have the best of both worlds. Use Hive for more traditional heavy analytic queries but then perhaps HBase for more user-facing lighter queries. And to look ahead a little both Cloudera's Impala and Apache Drill will offer a much lower-latency SQL interface to data residing on HDFS so the fusion will become greater.
In the renaissance, how big were the dinosaurs? Did you have tiny ads?