How Hadoop’s Fundamental Problem-Solving Capabilities Are Applied in Data Science

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Hadoop is an open-source big data platform that is used for processing large scale data, both structured data and unstructured data. It is a highly scalable software framework designed to accommodate computation ranging from a single server to a cluster of thousands of machines. The main functionality of Hadoop is the storage of Big Data. It is the most the common system, based on Java, and an ecosystem of products, which apply a particular technique "Map/Reduce" to obtain results in a timely manner.

Hadoop and Data Science
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Hadoop and data science combine to form an entire data pipeline – managed by teams of data researchers, programmers, engineers, and businesspeople. Hadoop is a key tool for data science as it helps to transport data to different nodes on a system at a faster pace. On a larger scale, massive data collection, processing, and analysis require equally substantial storage and computational resources. This is where Hadoop plays a significant part. 


Hadoop exists in a four-part architecture supporting two basic functions. The modules are:

 

·        Hadoop MapReduce – The parallel processing module based on YARN

·        Hadoop Common – Essential utilities and tools referenced by the other modules

·        Hadoop Distributed File System – The the high-throughput file storage system

·        Hadoop YARN – The job-scheduling framework for distributed process allocation

 

Every company needs data scientists to comb through their data and find better ways to improve their operations and enhance their productivity. The Apache Hadoop ecosystem is a popular and powerful tool to solve big data problems. Hadoop is specially designed for two core concepts: HDFS and MapReduce. Both are related to distributed computation. MapReduce is believed as the heart of Hadoop that performs parallel processing over distributed data.

 

Take a look at the reasons why companies are adopting Hadoop-

·        Organizations begin to utilize Hadoop when they need faster processing on large data sets, and often find they save the organization some money too.

·        Components of Hadoop allow retaining data for a long.  Instead of storing data for short periods, data could be stored for the longest periods with the liberty of analyzing stored data as necessary. The longevity of data storage with Hadoop reflects its cost-effectiveness.

·        Hadoop doesn't enforce a schema on the data it stores. It can handle arbitrary text and binary data. So, Hadoop can digest any unstructured data easily.

 

Businesses are relying on Data Science to gain a competitive advantage and data analysis has become a corporate priority. Many decisions have taken from the extraction of a huge amount the relevant data which helps to come to the conclusion easily. The most important factor of using Hadoop is its fault tolerance as it can store and distribute very large data sets across hundreds of inexpensive servers that operate in parallel.

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