Hadoop Platform: Usage and Significance Research Paper

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Table of Contents

Hadoop and its Operations
Significance of Big Data Management
Hadoop’s Big Data Management Security
Hadoop as A Recommended Big Data Analytics Technology
Hadoop Code and The Significance
Hadoop Interface Tools and Their Roles
The List of Companies using Hadoop Technology
References

Hadoop and its Operations

Hadoop is an open-source platform commonly used for the storage and processing of big data. It is an approach that enhances the efficiency scale in analyzing massive datasets across different connected computers. Although a single computer can process big data based on schematics, networking improves productivity in processing information from dynamic sectors. Hadoop is a technological tool essential in the modern world due to the increasing gathering and processing of big data (Data Flair, 2022). Different institutions use the dataset for dynamic purposes, such as analyzing the behavioral indicator during the purchasing process. Therefore, it is the responsibility of executive teams to establish critical frameworks that enhance the derivation of in-depth details concerning the distinctive variables. The lack of clarity fosters subjective decision-making that results in bias due to inadequate knowledge of vital topical issues.

Significance of Big Data Management

Different organizations participate in the extensive data analysis due to the outcome concerning the derived apt insights on emerging trends in an industry. Over the decades, a significant percentage of the population incorporated systems across distinct sectors, such as healthcare and law-and-order practices. In this case, it is easier for a person to use big data to analyze a particular conceptual framework. Therefore, dataset management plays a crucial role in advocating for understanding consumer behavior and pattern. In a different spectrum, the processing of the quotient is an empowerment tool for artificial intelligence systems (Rowe, 2016). As a result, it is the responsibility of distinct stakeholders to exploit the massive dataset to enhance companies’ competitive advantage in marketing. Primarily, corporates invest in data analytics to attain vital information regarding their clients and the evolutionary trends. Notably, the executive team develops marketing strategies and product diversification features based on the preferential baseline.

Hadoop’s Big Data Management Security

Hadoop technology is a secure way to manage data due to the significant intersection of adept coding and storage structures. It is essential to distribute the big data processing across different computer mainframes to reduce the risk of overloading one computer. In this case, networking also fosters gathering specific information from various sectors and processing crucial details (Data Flair, 2022). An excellent example is the utilization of the framework in a healthcare institution. On the one hand, massive dataset processing in a clinic fosters the derivation of statistics based on clientele recovery and re-hospitalization rate. On the other hand, medical practitioners invest resources on common issues causing the prevalence of certain conditions. One of the insights that rely on extensive dataset management is understanding the dynamic lifestyle habits and implications to personal well-being. The exploitation of the dataset fosters an overview regarding the causative agents to physicians’ demand for more health-based details. Primarily, it is safe to use Hadoop to manage big data since it enlightens the stakeholders concerning specific issues within a single spectrum, such as healthcare quality.

Hadoop as A Recommended Big Data Analytics Technology

I would recommend using Hadoop to manage big data due to the importance of enhancing productivity. Efficiency in utilizing massive datasets empowers corporates with insights regarding distinctive approaches to consumer behavioral patterns. Apart from the necessity of understanding the clientele, other institutions sufficiently comprehend the changes in a particular system. An excellent example is the gathering weather changes and sequence as a formative strategy to demonstrate the unprecedented climate change. In this case, it is key stakeholders’ responsibility to uphold the relevance of Hadoop due to the intersection of dynamic details. Hadoop technology significantly contributes to the execution and implementation of action plans objectively (Data Flair, 2022). Over the decades, the intensification in technological use rendered the increase in the scoop of big data daily. As a result, data analytics corporates seek approaches, such as using Hadoop to enhance the productivity scale on adjusting to the evolutionary cycle. Therefore, Hadoop is recommended to boost the performance rationale across different professional practices and sectors.

Hadoop Code and The Significance

There are different coding mechanisms within the Hadoop system for optimal outcomes. The two standard codes include mapper and reducer, which mainly enhance datasets’ compilation under the spectrum of divide and conquer. Therefore, programmers exploit the coded language to analyze distinctive massive datasets for specific information. Below is a description of the mapper and reducer codes and their significance to the processing.

Mapper code

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publicstaticclassMap extendsMapper<LongWritable,Text,Text,IntWritable>{
publicvoidmap(LongWritable key, Text value, Context context) throwsIOException,InterruptedException {
String line = value.toString();
StringTokenizer tokenizer = newStringTokenizer(line);
while(tokenizer.hasMoreTokens()) {
value.set(tokenizer.nextToken());
context.write(value, newIntWritable(1));
}

The code represents the class-map used to extend the mapper defined in the MapReduce framework. In this case, the program significantly utilizes the coding to establish inherent values on input and output keys after the declaration by using the angle brackets. In this case, a java code is used in tokenizing every word and assigning a hardcoded key equivalent to 1.

Reducer Code

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publicstaticclassReduce extendsReducer<Text,IntWritable,Text,IntWritable>{
publicvoidreduce(Text key, Iterablevalues,Context context)
throwsIOException,InterruptedException {
intsum=0;
for(IntWritable x: values)
{
sum+=x.get();
}
context.write(key, newIntWritable(sum));
}
}

The above is a reducer class code that enshrines the apt use of extension in the ClassReducer. The input and output keys significantly attributed to the key-value pair and the necessity of boosting the performance scale. It is vital to establish the distinctive role of a single reducer using unique words to get a final answer. The Hadoop coding operates under the divide and rule perspective. In this case, the programmer focuses on the intersection of mapper and reducer to enhance the analysis of a weather update and the prediction of changes in the future. Therefore, the coded approach is an essential attributable baseline for corporates in improving the optimal gathering and analysis of big data.

Hadoop Interface Tools and Their Roles

The two primary Hadoop tools encompass HDFS and YARN, which play a crucial role in the performance scale of the system. HDFS focuses on the storage of big data and the ease of accessibility. In this case, Hadoop ensures the apt function of the storage units for the information gathered and prepared for assessment. YARN is another tool used for processing the collected big data (Data Flair, 2022). The element utilizes different codes in the management and analyses of the dataset to derive crucial insights. The lack of clarity risks miscommunication and inefficient execution of action plans. Therefore, it is the responsibility of programmers to establish core values on intersecting the YARN and HDFS values for adept productivity across the various institutions. The coordination of storage and processing units fosters intensifying the derivation of crucial details. As a result, it is essential to ensure optimal professionalism in the coding structure.

The List of Companies using Hadoop Technology

Marks and Spencer
Royal Mail
British Airways
Royal Bank of Scotland
Expedia
Amazon Web Services
ScienceSoft
Pivotal
IBM
Microsoft
Hortonworks
MapR
Datameer
Adello
Hadapt
Kamasphere
NG Data

References

Data Flair. (2022). 12 frequently used Hadoop HDFS Commands with Examples & usage. Web.

Rowe, W. (2016). Hadoop Tutorial for Beginners: Hadoop Basics. [online] BMC Blogs. Web.

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