Few initiatives to handle Big Data Challenges:
Some RDBMS has tried to solve these challenges which include SAP Sybase IQ. It uses column-store technology, enabling it to compress data efficiently, and a parallel processing approach on multiple servers to handle multi petabyte data stores.
Database appliances have been built to address these problems, including the SAP HANA database and SAP HANA software, which have demonstrated a greater than 20x compression rate where a 100 TB five-year sales and distribution data set was reduced to 3.78 TB and analytic queries on the entire data set ran in under four seconds.
Big Data and Hadoop:
As Big Data overwhelms traditional databases, storage and more, companies are looking to exploit new tools like Hadoop. The reality is that you want to avoid single device bottlenecks, since they inhibit scaling. Hadoop uses map-reduce to spread analytical processing across armies of commodity servers.
The other approach would be to use non-relational data store - HADOOP..