These days companies are seeing greater volumes of customer, ERP, and other types of data streaming into their organizations. And this is placing an immense burden on their storage systems.
For its part, IDC forecasts that global digital data growth is expected to undergo 50-fold growth from 2010 to 2020. Meanwhile, the volume of business data is growing at an average rate of 36% per year, according to research by Aberdeen Group.
The three key challenges often associated with big data are the “3 Vs”: volume, velocity, and variety, as Aberdeen and other industry experts note.
“Ever-growing volumes of data must be stored and accessed, then captured, processed, analyzed and delivered to knowledge-workers with unprecedented velocity, in a broad variety of different formats,” according to Aberdeen.
One effective technique organizations can use to deal with the increasing volume of data as well as velocity demands is in-memory analytics. In-memory analytics tools help companies deal with data volumes by moving data as close as possible to the processors.
Traditionally, when companies try to use data for analysis, the information is accessed from a database, an action that depends on the speed of the disk drive where the data is stored. The process is further prolonged by any latency that occurs in the transfer of the data through the input/output connection that sits between the storage device and the server.
In-memory computing leverages multi-core processors that are used with the current breed of servers along with large amounts of local random access memory (RAM) that’s available on servers. The result: zero latency issues with data transfer, enabling organizations that use in-memory analytics to conduct real-time (or near real-time) analysis on terabytes of vital business data.
In-memory analytics tools are helping companies across a wide range of industries to access and analyze data faster than conventional analytics tools, thus enabling decision makers to find answers quickly while gaining an edge in time to market with new products and capabilities.
Intense competition in high-tech manufacturing forces companies to contend with short design-to-market deadlines. Many manufacturers struggle to effectively and efficiently use high volumes and varieties of data inputs from manufacturing equipment, testing systems, operational, and supply-chain feeds.
Further complicating these challenges for some high-tech manufacturers is the fact that only small percentages of potential users of specialty software, such as yield management systems, are using them effectively. In-memory analytics can help engineers and researchers access data from multiple systems more easily and solve problems faster.
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