How In-Memory Big Data Becomes A Strategic Asset

Do you have any idea about the approximate size or even total amount of data we are dealing with everyday? Don’t have a heart attack when you find the size is around 2.5 quintillion bytes. In terms of computing, big data represents the exponential growth of ginormous amounts of data which are being created by us on so many different purposes. You will find multiple sources, if you try know from where such large volume of data are coming.

For example, your blog posts, email attachments, involvement and interaction with social media platforms, online transactions and shopping records and all such information are part of Big Data. Besides of these, more than thousands of sensors are collecting and gathering data/information for different purposes like weather forecasting, stock market and GPS signals are some other sources which are feeding more than trillions of bytes to Big Data.

Is Big Data Problematic?

Big data might have been an extreme problem for us if we didn’t have such advanced computing and IT infrastructures as we do now. It is mainly the sophisticated data analyzing processes and large data storage capacity, which allow us to deal with big data in a more effective and efficient manner. The precision metrics of many research and development processes are getting higher, as the researchers can deal with such large volume of data from diversified sources. If you still confused about how big data can be considered as a strategic asset for many of use, then the following discussion will help you clear the issue.

Big Data In Business

The definition of doing business has changed over last 2/3 decades, as computer and information technology has become the most obvious and integrated part of any business. Information from big data can help you to identify and prioritize your customers. It may not possible for you to precisely calculate both risk and business strategies within a short period of time, unless you get such seamless flow of information from datacenters or datasets. There is no alternative of detecting fraudulent activities to minimize the risk factors and doing business in a fair way. Here, you also need those information for detecting such fraudulent activities, which are available inside Big Data. You can take your business to many steps ahead by analyzing the customer data and develop a better business strategy from there.

GridGain Middleware Handles Big Data

GridGain is a middleware software which is mostly developed on JVM (Java Virtual Machine) and designed to develop distributed applications. In other word, you may also consider Grid Gain as very high performance cloud computing, which is capable of dealing with critical and complex data and process analysis. The flexible development environment of GridGain allows the developer to come up with both standalone and distributed applications. With the help of GridGain, your applications can co-locate both structured and unstructured information from big data. Distributed thread pools and cached distribution of data are two competitive advantages, which you can get from GridGain. You can also proceed with your SQL queries which have stored and cached from big data, by using GridGain. The integration of GridGain with big data will let you get a distributed computation and data analysis facility. Such distributed data storage and processing capability with co-location facility has made big data an obvious part of GridGain.

Posted by Greg Henderson, an Internet Marketer and SEO Associate for a cell phone lookup site FreePhoneTracer.com, and an find an email address site EmailFinder.com.

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3 Responses to "How In-Memory Big Data Becomes A Strategic Asset"

  1. sanchit says:

    Hello Greg Henderson,
    Really you share a resourceful blog, i think nobody think about it. you are right.
    it a very problematic and unhealthy.
    we must be strategic about that and give attention on it.

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  2. Big data safety and efficiency is all about software and hardware! It’s like having SSD instead of HDD in your PC. Simple example but very clear)

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  3. Nice blog Greg, big data showing some times problematic. you nicely describe the big data one by one.

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