S437 A8

docx

School

Indiana University, South Bend *

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Course

S435

Subject

Information Systems

Date

Oct 30, 2023

Type

docx

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Uploaded by EarlFreedomMonkey29

Hadoop and MapReduce have revolutionized big data processing by providing a powerful framework for handling massive amounts of data. HDFS, YARN, and MapReduce are components of Hadoop's architecture that work together to store, manage, and process data across a cluster of computers. The components of MapReduce's mapper, reducer, and combiner process and aggregate data in parallel across multiple machines in a cluster. The retail industry is one real-world business application where Hadoop and MapReduce have been used effectively. Walmart, for example, uses Hadoop to process massive amounts of data from millions of transactions that occur in its stores every day. Walmart can use Hadoop to analyze data to identify buying patterns and trends in order to optimize inventory management, pricing, and store layouts. The advantages of using Hadoop and MapReduce for big data processing are numerous. One of the primary benefits is scalability, which means that businesses can process large amounts of data by adding more servers to the cluster. Hadoop is also a cost-effective solution because it is open- source software, allowing businesses to process data without incurring costly licensing fees. Additionally, Hadoop is fault-tolerant, meaning that it can handle hardware failures or other types of failures and still keep data secure. Despite the advantages, using Hadoop and MapReduce for big data processing has its drawbacks. To begin with, the complexity of these technologies necessitates the use of specialized knowledge to set up and maintain. Second, Hadoop and MapReduce are batch processing systems, which means they are not suitable for real-time data processing. This can cause latency or delays in data processing. Finally, data security is a major concern because Hadoop and MapReduce lack built-in security features. As a result, businesses must implement security measures to safeguard sensitive data.

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