hadoop

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Date 2020-03-15

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According to the author, the Hadoop has evolved from apache Hadoop that was developed for the storage and large-scale processing of data

sets on the cluster of commodity hardware. Currently, Apache Hadoop is composed of four modules that are designed with the assumption

that hardware machines failures are common hence automatically solved in software by integrated framework. They include Hadoop

MapReduce for large scale information processing derived from the Google file system, Hadoop common that contains storage space and

utilities required by other modules. The third module is Hadoop yarn that is responsible for managing computer resources in the various

cluster while using them for scheduling user applications. Lastly is the Hadoop distributed file system that is responsible for data storage on

the commodity machines and offering high aggregate bandwidth. According to the author, the use of Hadoop has improved data processing

efficiency and effectiveness through creating data awareness between task track and job track. Dhankhad, S. (2019, April 29). A Brief

Summary of Apache Hadoop: A Solution of Big Data Problem and Hint comes from Google. Retrieved from https://towardsdatascience.com/a-

brief-summary-of-apache-hadoop-a-solution-of-big-data-problem-and-hint-comes-from-google-95fd63b83623 The author focuses on the

problem introduced by the use of big data, how Hadoop can help solve challenges, the framework of apache Hadoop, and how they work. The

biggest challenge with big data arises as a result of the problem of arranging big data in a specific format. Most of the big data are

unstructured, limiting the effectiveness of working with structured data. Big data usually have four features that make the large and

unregulated, which include volume, variety, velocity, and veracity. The introduction of Hadoop can help solve these problems big data through

the use of Hadoop modules such as MapReduce. It provides algorithms that distribute big data in small pieces and assign those small pieces

to each network in a structured manner. It involves four processes, which include ingesting, processing, analyzing, and accessed. It uses

various frameworks such as HBase, Sqoop, Flume, hue, spark, pig, impala, hive, and Cloudera search enable faster transfer of big data

among nodes. Floratou, A., Minhas, U. F., & Özcan, F. (2014). Sql-on-hadoop: Full circle back to shared-nothing database

architectures. Proceedings of the VLDB Endowment, 7(12), 1295-1306. https://dl.acm.org/doi/abs/10.14778/2732977.2733002 The article

focuses on the application of Hadoop, especially SQL query and MapReduce. According to Floratou, Minhas, & Özcan (2014), most

organizations have adopted the use of Hadoop as a central data repository for various information coming from multiple stakeholders and

business units. With the application of SQL quarry process analytic and MapReduce, the organization has been able o centralize its

information system, including operational systems, sensors, smart devices, social media, and web, among other applications. Integration of

the Hadoop MapReduce module and SQL process, business is able to manage and run a deep analysis of their centralized data to gain

predictable insight from the information, including understanding both structured and unstructured data. The result shows that collaboration of

impala database like architecture, and MapReduce provide significant gain to the organization. Shared-nothing database architecture provides

a beneficial element to the organization when integrated with Hadoop and SQL. The ability of Hadoop to store and schedule extensive data

has made it more applicable to business. Gao, S., Li, L., Li, W., Janowicz, K., & Zhang, Y. (2017). Constructing gazetteers from volunteered

big geo-data based on Hadoop. Computers, Environment and Urban Systems, 61, 172-186. Retrieved from:

https://arxiv.org/ftp/arxiv/papers/1311/1311.7676.pdf The articles focus on the development of gazetteers using big data based on Hadoop.

According to the author, big data have reached an age where they can be used to build gazetteers using data-driven approaches from the

web. The availability of Hadoop MapReduce has simplified the process of using big data and the web. This is because it can analyze

extensive data through various clusters hence reducing the processing time compared to the traditional method. In recent decades, the use of

the social web has emerged as a new form of crowdsource gazetteers enabled by increased adoption of Hadoop. It has led to collaboratives

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mapping platforms and socially constructed places such as Facebook. MapReduce minimizes the problem of big data such as large volumes,

varieties of data, and updating velocity to promote analysis and interpretations of big data. Hadoop is used to process big geodata and

facilitates the creation of crowdsource gazetteers, shows how multiple valuable features of gazetteers can be scaled up and integrating with

other technology. Ghazi, M. R., & Gangodkar, D. (2015). Hadoop, MapReduce and HDFS: a developers perspective. Procedia Computer

Science, 48(C), 45-50. Retrieved from: https://core.ac.uk/download/pdf/81181227.pdf The use of Hadoop has increased drastically in the

modern days due to the organization finding a new way of utilizing technology more efficiently and simply that works well in distributed

environments. According to the authors, the Hadoop modules are highly compatibles with thousands of machines and massive information set

using available hardware. This has made most of the business and developers change the perspectives concerning the use of Hadoop.

According to the authors, most developers have changed their perspective concerning Hadoop due to the ability of the MapReduce and

Hadoop distributed file system to develop a model that hides all the complexities for big data analytics. In the modern-day, information has

because of useful resources for the organization if it has to survive in business. Therefore, there is an increased need to fast software to

gather extensive data, analyze, examine, and process huge data of unstructured form to extract the required information. Gummaraju, J.,

Mcdougall, R., Nelson, M., Griffith, R., Magdon-Ismail, T., Cheveresan, R., & Du, J. (2019). U.S. Patent No. 10,193,963. Washington, DC:

U.S. Patent and Trademark Office. Retrieved from: https://patentimages.storage.googleapis.com/48/cd/b5/d914009377b091/US10193963.pdf

The articles concentrate on how Hadoop allow distribution of computing platform by allocating the computing task cross various cluster of the

developed software. Hadoop enables analyze of large workloads such as big data set

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Apache Hadoop is a collection of open-source software utilities that facilitate using a network of many computers to solve problems

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