Why In-Memory Computing Technology Will Change How We View Computing

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Nikita Ivanov founded GridGain Systems in 2007, which is funded by RTP Ventures and Almaz Capital. Nikita has led GridGain to develop advanced and distributed in-memory data processing technologies, including a Java in-memory computing platform.

NikitaIvanov-tnNIKITA IVANOV
GridGain Systems

Big Data is BIG — in fact, it’s too big to be of any use without the technology capable of handling it. With businesses creating 2.5 quintillion bytes of data every day, it’s nearly impossible for them to gain timely, actionable insights from it without the right tools. In order to remain competitive, organizations must be able to make data-derived decisions from the unwieldy amounts of information springing from countless sources: geothermal sensors to social media sites, CRM data, GPS, supply-chain, and virtually any other segment of an organization where information can be analyzed. Unfortunately, most organizations created and ingest much more data than they can make sense of. Think of it as organizational sensory overload.

In order to get past organizational sensory overload and actually derive intelligence from its data, businesses must rethink computing. Traditionally, data is brought to the computation – a time-consuming, resource-intensive process. Due to the way that data is stored and accessed with traditional computing, latency actually increases as more data is placed in storage.

Think of this bottleneck as traffic caused by multiple lanes on a highway merging into a single lane during rush hour. As the number of cars on the highway increase, so too does the length of time stuck in traffic, likewise, with traditional computing, there is a positive correlation between the amount of data stored and latency.

On the other hand, there is In-Memory Computing (IMC). IMC technology essentially reverses a fundamental tenet of computing by bringing the computation to where the data is – in memory, which is orders of magnitude faster and frees resources. It is this reduced computational latency that makes deriving live action from streaming data possible. In fact, IMC is the only way to address data in-flight.

Moving Beyond the Traditional Methods

IMC technology makes it possible for organizations to conquer challenges that are beyond the capabilities of traditional technology. With IMC, businesses and organizations are able to provide real-time answers, while looking across vast amounts of data, expanding the possibilities for real-time decision making. This means that businesses could then use this data to better understand customer preferences and behaviors, and detect other correlations that they’d otherwise overlook.

Examples of data-oriented tasks that require the high performance computing capabilities of In-Memory Computing include:

  • In the financial services industry, organizations may only have a fraction of a second to analyze a wide range of datasets in order to detect fraud and/or market risk.
  • In the oil and gas industry, companies must analyze large volumes of real time data to monitor pipelines and seismic sensors.
  • In the logistics industry, real time data is used to calculate pickup and delivery routes, based on package location, traffic and weather conditions.
  • In sales and marketing, businesses must be able to track every single item in every store, and analyze sales patterns and trends in real time.
  • In healthcare, organizations could use real time data to diagnose and treat patients.

At the rate organizations are creating data, it’s unsurprising that much of it goes unused and insights go undiscovered. If enterprises could move a hundred, or even a thousand times faster, the possibilities it could derive from its data are endless. With the adoption of IMC, these possibilities are becoming realities every day.

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