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.
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