Raghav Raghavendra Pratap Singh
Raghavendra is the Assistant Marketing Manager at Sigmoid. He specializes in content marketing domains, digital and social media marketing.
Raghavendra Singh
He is the Assistant Marketing Manager at Sigmoid.
Hadoop and Data Analytics

Given the clear connection between Big Data and IoT, it isn’t really a surprise that Data Analytics is emerging as one of the fastest growing economy. Data from Big Conglomerates to small-scale business is analyzed to capture insights which can help in redefining the enterprise strategies and improving business value.

So, what is Big Data?
According to Gartner, the definition of Big Data reads, “Big data is high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision-making, and process automation.” Big Data analytics find insights that help organizations make better business decisions. Simply stated it’s a large collection of data, both structured and unstructured, generated from different devices and applications which is growing rapidly every year.

Traditional system has difficulty in dealing with large volume of data and this is where Hadoop comes in. Hadoop is an open-source framework written in Java for storing, processing and performing highly parallelized operations on Big Data. Apart from providing massive storage for data, it provides the capability to deal with high volume, velocity, and variety of data.

So, what’s the history behind Hadoop?
With the inception of the internet, the web grew from few to million web pages from the 1900s to 2000s and entering search results by human became tedious and needed automation. Doug Cutting and Mike Cafarella started working on open source web engine called NUTCH for faster data distribution and collection.

In 2006, Cutting joined Yahoo, combined NUTCH distributing and processing part with Google File System(GFS) to create Hadoop. In 2006, Hadoop was released by Yahoo and today is maintained and distributed by Apache Software Foundation (ASF).

So, when many organizations decided that their mining and analyzing tools cannot handle big data, they came up with the solution of building Hadoop clusters. Hadoop clusters are a special type of clusters designed for analyzing and storing a large volume of unstructured data. It distributed the data analysis workload across multiple cluster nodes which work in parallel to process data.

There has been a lot of discussion among experts in Big Data Analytics field over Hadoop data analytics engine and its performance in the business application.

Going beyond searching millions of web pages and returning relevant results, many big organizations like Google and Facebook are using Hadoop analytics engine to store and manage their huge data and in data analytics because of the following advantages of Hadoop-

1) Low cost- Since traditional relational database management system is expensive and have limited scale to process huge data, Hadoop offers cost-effective storage as it is an open-source framework and Hadoop cluster uses commodity hardware to store large quantities of data and keep multiple copies to ensure the reliability of data.

2) Scalability- System can be designed to handle more data by simply adding nodes and distributing a large set of data across hundreds of servers that operate in parallel which require little to no administration.

3) HDFS (Hadoop Distributed File System)- Hadoop has its own distributed file system which is used for organization of files.

4) Flexibility- Hadoop allows easy access to new data as well as new sources such as Social media, emails and clickstream data for saving the different type of unstructured data (text, image, and videos) and structured data. So, unlike the traditional database, you don’t have to preprocess data before storing it.

5) Fault Tolerance- the Major advantage of using Hadoop is Fault tolerance i.e. protection against hardware failure. When data is received through a node, multiple copies of this data is made on other nodes automatically in the cluster which in any event of failure can provide with backup copy or job can be redirected to other nodes.

6) Computational Power- Hadoop provides high computing power with the help of a distributed file system basically which can map data located in a cluster. Since the data processing tools are on the same server as the data, it results in high processing of data. Petabytes and terabytes of unstructured data can be efficiently processed using Hadoop in a matter of minutes and hours respectively.

So, when it comes to the handling of a large volume of data and parallel processing of data in a safe, fault tolerant and cost-effective manner, Hadoop is the king of analytical tools.

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