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Hadoop Addressing Challenges of Big Data Research (Essay Sample)


addressing the challenges of Big data: hadoop.


Hadoop: Addressing Challenges of Big Data
Institutional Affiliation
Hadoop: Addressing Challenges of Big Data
Undeniably, every organization operates on data. Besides, the statistics are quite essential as how they are handled dictates the success or failure of those behind the information. However, despite being quite useful to the entity concerned, such facts may impose many challenges dealing with. As a result, any future-oriented 21st-century business would opt for a tool that it can utilize to evade such encounters. Simply put, the increased complexity of dealing with big data continue to force many enterprises globally into using software programming frameworks, especially “Hadoop,” so as to diminish cost and amount of time spent on loading such voluminous information into an interactive database for analysis.
Challenges and Solutions
Big Data
The term “Big data” denotes a universal jargon used when describing the vast quantity of statistics that is both semi-structured and unstructured (Kleespies & Fitz-Coy, 2016). As mentioned earlier, such pieces of information are often generated by the company but consume a lot of money and time to load into an appropriate database for scrutiny. In other words, due to the nature of such data to enlarge so rapidly, many enterprises always find it not easy to tackle using the regular analysis tools. Therefore, such pieces of facts must be partitioned prior to examining them.
Platform architecture. Essentially, Hadoop is comprised of two principal components, namely HDFS and MapReduce as displayed in the diagram that follows. While the latter is the processing section concerned with job management, the former, which denotes “Hadoop Distribution File System,” is charged with the role of storing all facts redundantly needed for computations (Lin & Dyer, 2010). At the same time, projects are set of tools managed by Apache to offer support in the task correlated to Hadoop. Therefore, the diagram below denotes the entire architecture.
Adopted from Singh & Kaur, 2014.
Hadoop describes a platform developed to help in tackling the challenges of processing and analyzing Big data. According to Singh and Kaur (2014), Hadoop is “An open source cloud computing platform of the Apache Foundation that provides a software programming framework called MapReduce and distributed file system, HDFS” (686). Having been written in Java, the framework supports the running of software on voluminous statistics. Consequently, it can address primary encounters produced by Big data. Therefore, the diagram below is a depiction of the biggest challenges associated with huge statistics.
Adopted from Singh & Kaur, 2014.
Volume. As an entire ecosystem of projects, Hadoop works to deliver a standard set of facilities. Unlike the traditional approach, huge data is first subdivided into relatively smaller segments so as to ensure effective and efficient handling of statistics. Undeniably, the tool transforms product hardware to coherent services, which then store petabytes of figures steadfastly (Mayer-Schönberger & Cukier, 2014). Furthermore, as data gets segregated, likewise, the software breaks the computation into smaller pieces. Besides, such information is subsequently processed efficiently via vast deliveries. Therefore, mentioned platform offers a framework that is capable of scaling out horizontally to subjectively bulky records to tackle bulks of statistics.
Velocity and variety. Unlike other tools, Hadoop is widely applauded for its ability to partition data and compute across numerous hosts consistently. Undeniably, Big data is often propagated with excessive swiftness and multiplicity. Besides, the instrument is capable of performing application calculations in matching close to their mandatory figures. Given the existence of an upper boundary to an amount of facts that can be processed, it is undebatable that scaling up such data is quite a challenge (Mayer-Schönberger & Cukier, 2014). Worth noting is the case that Hadoop is both reliable and redundant; thus, it can automatically replicate facts in the absence of operator’s intervention in the event of any failure. Therefore, the framework is designed to tackle gigantic pieces of information regardless of their furious incoming rate.
Furthermore, being exceptionally powerful in accessing raw information, Hadoop is “primary batch processing centric and makes it easier for distributed application with the aid of MapReduce platform model” (Kshetri, 2014). Subsequently, its commodity hardware can reduce the cost associated with purchasing unique lavish hardware structures. In other words, challenges of velocity and variety linked with d...
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