Is Hadoop necessary for data scientist?

Is Hadoop necessary for a data scientist?

• Hadoop is necessary for Data Scientists
• What is Hadoop?
• Brief on Hadoop
• The history of Hadoop 
• Benefits of Hadoop for Big Data
• Data scientists need Hadoop but not necessary

If a question is raises in your mind, Is Hadoop necessary for data scientists? 

The answer will be: 

Yes, of course, Hadoop is necessary for Data Scientists.

Data Science is multiple interdisciplinary fields including mathematics, statistics, and programming. Data science is one of the fastest-growing sectors because there is a large number of data to store, clean, process, and interpret. 

Data Scientists are extracting, preparing, analyzing, and generating predictions from the history of data. It is called an umbrella term that involves every technology that uses data.

Machine Learning is a part of Data Science that perform much better with a huge number of the dataset. Hadoop is playing as an important tool for Data science when the volume of data overcomes the system memory and the need to distribute the data across multiple servers in the business. As a result, Hadoop helps a Data scientist transport data to multiple nodes on a system server fast.

Let's know about Hadoop.

What is Hadoop?

Definition of Hadoop

Hadoop is an open-source software platform that distributed storage and processing of very large data sets on computer clusters built from hardware commodities. Hadoop is a Java-based framework. 

An open-source means free software platform is just a bunch of software that runs on a cluster of computers. So, it leverages the power of multiple computers to actually handle big data for distributed storage. Hadoop provides the idea of Distributed storage limited by a single hard drive. If you're dealing with big data, you might be getting more than terabytes of information every day. You can keep adding more and more computers to your cluster and their hard drives that will become a part of your data storage.

Hadoop gives you a way of viewing all of the data distributed across all of the hard drives in your cluster as one single file system. If one of those computers destroys your data set, Hadoop can handle that because it's going to keep backup copies of all your data in other places in your cluster. It can automatically recover and make that data is very resilient and very reliable.

It stores a vast amount of data across the entire cluster of computers to distribute the processing of that data as well. Similarly, Hadoop provides a parallel manner to do it. It is a very large data set that can not be possible to manage by a single PC. All the computer clusters are built from hardware commodities. In short, you can rent from Amazon Web Services, Google, or any of the other vendors that sell cloud services.

BRIEF ON HADOOP

1. Doug Cutting created Hadoop of Yahoo

1. Nutch search engine project built

1. Mike Cafarella joined

1. GFS, GMR & Google Big Table base

1. Named after Doug cuttings kid's toy elephant

1. Open Source platform- Apache

1. Powerful, Popular & Supported

1. Handle Big Data framework

1. The distributed, scalable, and reliable computing system

1. Written in Java

The history of Hadoop 

Hadoop was not the first solution to this problem. Doug Cutting had created Hadoop to build his search engine called Nutch. Mike Cafarella had joined him. Hadoop was the name of Doug cuttings kid's toy elephant. So the project was named after the yellow stuffed elephant named Hadoop. 
In 2003-04 Google published three papers based on Hadoop: 

• Google File System GFS

• Google MapReduce

• Google Big Table. 

So Hadoop was developed originally by Yahoo. They were building something called Nutch which was an open-source web search engine at that time.

Benefits of Hadoop for Big Data

• Resilience
• Scalability
• Low cost
• Data diversity
• Fast

GFS kind of informed the Hadoop storage system turned into MapReduce. It is a Hadoop solution for distributed storage of information. So GFS is basically the inspired Hadoops distributed data storage and MapReduce is the inspired Hadoop's distributed processing.

You know the rest is history. Since 2006 the Hadoop has continued to evolve. Hadoop has grown continuously more and more.

Hadoop is used as a lifesaver for big data and analytics. We emerge a meaningful pattern and get a better decision after gather data about people, processes, objects, tools, etc.

How does Hadoop overcome the challenge of big data? The benefits of Hadoop are as follows.

There is a replicate of data stored in any nodes of the cluster. So there is always available a backup of stored data when one node goes down in the cluster.

Hadoop is a highly scalable distributed environment that has big data storage across hundreds of inexpensive servers. But, traditional systems have a limitation of data storage. The setup could be easily expanded when needed more servers and stored up to multiple petabytes of data.

As we know, Hadoop is an open-source platform, and no need to procure a license. The cost of Hadoop is significantly lower than relational database systems. 

HDFS capable to stored in different data formats like structure, unstructured (videos), and semi-structured (XML files). There is not required to validate against a predefined schema while storing data. Although, we can dump the data in any format.

Hadoop distributed file system is based on Hadoop's unique storage method. located on a cluster that is mainly called 'maps' data. The data processing tools are often available on the servers where the data is located. As a result, it offers much faster data processing. Hadoop is capable to process terabytes of unstructured data within a minute.

Data scientists need Hadoop but not necessary

Powerful algorithms and computational resources are needed to use much business analysis. But Hadoop is not always supported to achieve it.  In this case, Apache Spark is another alternative to solve big data for the organization. Apache Hadoop is an ideal choice but sometimes it losses the hope for complex analytics in Machine learning. Hadoop might be incapable when recommending millions of products and customers or processing sequences of genetic data on huge arrays. Hadoop is always not considered a data-parallel analytics task.