Data is exploring at large organization. So is the adoption of Hadoop. Hadoop’s potential cost effectiveness and facility for accepting unstructured data is making it central to modern, “Big Data” architectures. Yet, a significant obstacle to Hadoop adoption has been a shortage of skilled MapReduce coders.
In your world – numbers and data can save lives. Minutes and seconds absolutely matter. Whether engaged in genome sequencing, drug design, product analysis or risk management, life sciences research teams need high-performance technical environments with the ability to process massive amounts of data and support increasingly sophisticated simulations and analyses.
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.
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.