To say that Big Data is the real deal would be somewhat of an understatement. Businesses that have not yet adapted to this new-normal, are either preparing for the shift or in trying to just match the pace of their competitors, which doesn’t seem to be easy. Whether people choose to believe it or not, raw information ought to now take the shape of big data. But the million-dollar question becomes, how to extract immense chunks of information of data centers? The question is being answered by the advancements in yet another dimension called the Internet of Things. Ask an average big data engineer of the usefulness of a M2M (Machine to Machine) network.

As per an IDC prediction, there are an estimated 20 Billion devices that communicate with each other. By the year 2025 as much as 80 Billion devices shall be speaking with each other. This adds to the purpose of making the current and upcoming crop of data scientists potent in the knowledge of Iota. Research in this direction points towards a foreseeable future which is focusing on a human to machine interaction. Apache spark is one of the topics which are nowadays being covered by many data science certifications. This is in addition to machine learning and deep learning. Yet, not digressing from the topic, business leaders strongly believe that computer power kept aside, its’ managing the data which is highly challenging.

Why such a problem – A big data engineer often comes across a hurdle of a Hadoop Distributed File System (HDFC). As a recurring issue, the entire process takes a lot of time. It doesn’t make sense for so many resources to be allocated for this particular objective.

What then, is the solution for big data management?

A data and file management system is the solution to such woes. IBM for instance has come up with its All Flash Elastic Storage Server (ESS) 5.2. Talk of the town is that it has the capability to optimize data bandwidth performance by 60 percent. But that’s just the surface. In addition to that it offers the following:

  1. Effectively reduce workloads on the IT department by taking care of the backup needs.
  2. Big Data Applications & Hadoop can now be run on organizational storage.
  3. Data can now be transmitted across applications.

Having installed IBM’s spectrum scale, the ESS includes within its ambit all of the organization’s data lakes. This facilitates a single data ocean. A considerably high variety of protocols is supported by the same. A key feature of IBM’s server is that it allows for a Hadoop to directly access the data without the need of it to be copied into another HDFS.

With innovations like the one discussed above, the world of big data could be assumed to be stepping into the realm of maturity. On a global basis, it shall be very interesting to see the impact of such cognitive applications on human productivity.

News Reporter

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