A number of industries rely on high-performance computing (HPC) clusters to process massive amounts of data. As these same organizations explore the value of Big Data analytics based on Hadoop, they are realizing the value of converging Hadoop and HPC onto the same cluster rather than scaling out an entirely new Hadoop infrastructure.
Let’s start the conversation here: If you work with big data in the cloud or deal with structured and unstructured data for analytics, you need software defined storage.
The rapid, accelerating growth of data, transactions, and digitally aware devices is straining today’s IT infrastructure. At the same time, storage costs are increasing and user expectations and cost pressures are rising. This staggering growth of data has led to the need for high-performance streaming, data access, and collaborative data sharing. So – how can elastic storage help?
For a long time, the industry’s biggest technical challenge was squeezing as many compute cycles as possible out of silicon chips so they could get on with solving the really important, and often gigantic problems in science and engineering faster than was ever thought possible. Now, by clustering computers to work together on problems, scientists are free to consider even larger and more complex real-world problems to compute, and data to analyze.
With a hybrid approach to big data storage, companies can combine the high performance and speed capabilities of in-memory while solving the storage issues by putting the vast historical data sets on disk. By bridging available technologies, companies can deliver on all counts – including cost.
As compute speed advanced towards its theoretical maximum, the HPC community quickly discovered that the speed of storage devices and the underlying the Network File System (NFS) developed decades ago had not kept pace. As CPUs got faster, storage became the main bottleneck in high data-volume environments.