In-memory enables computing evolution

In-memory computing technology is gaining a lot of attention from different sectors, as end user organisations look to leverage the benefits of rapid processing of large volumes of data that in-memory solutions promise to deliver.

Tags: Big dataCNS ( Inc. ( Middle East and AfricaIn-memory processingOracle CorporationSAP
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In-memory enables computing evolution In-memory computing uses data stored in local memory to make much faster calculations than with traditional systems.
By  Keri Allan Published  July 2, 2014

In-memory computing databases are gaining a lot of attention of late, due to the high performance these solutions offer, making them a key enabler of big data analytics.

“As the cost of dynamic RAM (DRAM) chips has fallen, and their performance characteristics have improved, the amount of data that can be viably stored ‘in memory’ has grown. So much so, in fact, that the kind of large datasets that would have once sat in a disk-based data warehouse can now be placed in memory. From there, data can be retrieved at lightning speed,” explains Sandeep Kumar Thakur, Practice Head — Servers, Storage and Virtualisation at systems integrator CNS.

Several in-memory computing solutions are currently available in the Middle East, with large software vendors such as SAP, Oracle, IBM and Microsoft proactively promoting their offerings, and now a growing number of midsize players are also entering the field. A wide range of companies, from a variety of sectors, are embracing in-memory computing with financial services, telecoms, travel, media, entertainment and retailers amongst the most avid consumers.

“Most Middle East companies we talk to, especially medium to large enterprises, understand the value and importance of big data, and are actively seeking ways to prepare their business and IT systems,” says Paul Devlin, Director, SAP Platform Solutions, SAP MENA.

“Companies across many different market segments have implemented these types of solutions. We are currently working with oil and gas companies who are looking at data that is collected from tiny sensors on the sea bed to more accurately predict where the next significant oil find may come from. You have airlines that are looking to use data coming in real time from their engines in flight, to better predict a maintenance schedule. We are working with family businesses who want to understand the relationship a client has with them across their entire organisation, so that when a client buys a car, stays in one of their hotels or eats in one of their restaurants, they recognise and reward that customer appropriately. All of these examples involve using data to drive significant business value to an organisation and ultimately their customer,” he explains.

“Typically in-memory computing is used in these industries for different reasons and in the context of different business processes, but in general the motivation is to improve efficiency e.g. running certain processes faster, increase effectiveness by supporting decision making with more accurate and timely data and business process innovation,” continues Massimo Pezzini, VP and Research Fellow Gartner.

“Real time fraud detection in financial services, dynamic pricing in ecommerce or travel, preventive maintenance in energy and utilities or telecom and B2B ecommerce are all examples of applications developed using in-memory computing for speed, scale and insight.”

There are several in-memory computing solutions on offer, however the most popular appear to be appliances with built-in analytics modules. They can also be made available as diskless databases or be embedded into ERP, BI and cloud applications.

Shane Fernandes, BI Leader at Oracle highlights some of his company’s own products and what they can offer end users.

“Oracle Database In-Memory enables applications to automatically and transparently take advantage of in-memory processing. Customers don’t have to make any changes to their applications whatsoever; they simply flip on the in-memory switch, and the Oracle database immediately starts scanning data at a rate of billions or tens of billions of rows per second,” he notes.

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