I am trying to make a comparison between a system setup using Hadoop and HBase and achieving the same using Oracle DB as back end. I lack knowledge on the Oracle side of things so come to a fair comparison.

The work load and non-functional requirements are roughly this:  

A) 12M transactions on two tables with one simple relation and multiple (non-text) indexes within 4 hours. That amounts to 833 transactions per second (TPS), sustained. This needs to be done every 8 hours.

B) Make sure that all writes are durable (so a running transaction survives a machine failure in case of a clustered setup) and have a decent level of availability? With a decent level of availability, I mean that regular failures such as disk and a single network interface / tcp connection drop should not require human intervention. Rare failures, may require intervention, but should be solved by just firing up a cold standby that can take over quickly.

C) Additionally add another 300 TPS, but have these happen almost continuously 24/7 across many tables (but all in pairs of two with the same simple relation and multiple indexes)?

Some context: this workload is 24/7 and the system needs to hold 10 years worth of historical data available for live querying. Query performance can be a bit worse than sub-second, but must be lively enough to consider for day-to-day usage. The ETL jobs are setup in such a way that there is little churn. Also in a relational setup, this workload would lead to little lock contention. I would expect index updates to be the major pain. To make a comparison as fair as possible, I would expect the loosest consistency level that Oracle provides.

I have no intention of bashing Oracle. I think it is a great database for many uses. I am trying to get a feeling for the tradeoff there is between going open source (and NoSQL) like we do and using a commercially supported, proven setup.