In Big Data, computers and storage are organized in new ways
in order to achieve the scale required. The major storage companies just assert, without justification, that their old products are just fine. They're not.
Big Data is way bigger than the biggest
computers. In Hadoop, you solve the problem with an array of servers that
can be as big as you like. Hadoop organizes them for linear scaling. While most
storage vendors continue to plug their old centralized storage architectures
and claim they’re good for Big Data, the only solution that’s actually scalable
is an array of storage nodes, directly connected to the compute/storage nodes.
Hadoop organizes the computing to use such an array of compute and storage
nodes optimally, and it can grow without limit, for example to thousands of
nodes.
Hadoop has its own file system and database. The NAS systems
pushed by legacy vendors just add expense and slow things down. The old
centralized controller SAN systems are expensive and not scalable. Some vendors
promote how they are good for Big Data because they use lots of SSD – but
that’s way too expensive for Big Data. Others promote hybrid systems, but make
them affordable by playing tricks like compression, which just add expense and
slow things down.
Exactly one vendor has a storage system that is best for Big
Data: X-IO. X-IO has exactly the kind of storage nodes that Hadoop wants. Its
independent storage nodes are linearly scalable, without limit. Its software
makes spinning disks deliver at least twice the performance compared to any
other system. It can optionally incorporate SSD’s for even better performance,
without using the distracting tricks used by others – you just get better
blended performance, without effort. Because of the inherent reliability of the X-IO ISE units, you don't need as many copies of the data.
If it's Big, if it's Cloud, if it's virtual, the X-IO is the place to go for storage.