What is Hadoop?

Apache Hadoop is one of the most widely used open-source tools for making sense of Big Data. In today’s digitally driven world, every organization needs to make sense of data on an ongoing basis. Hadoop is an entire ecosystem of Big Data tools and technologies, which is increasingly being deployed for storing and parsing of Big Data.

Definition of Apache Hadoop

It is an open-source data platform or framework developed in Java, dedicated to store and analyze large sets of unstructured data.

With the data exploding from digital media, the world is getting flooded with cutting-edge Big Data technologies. However, Apache Hadoop was the first one that reflected this wave of innovation. Let us find out what Hadoop software is and its ecosystem. In this blog, we will learn about the entire Hadoop ecosystem that includes Hadoop applications, Hadoop Common, and Hadoop framework.

 

How did Apache Hadoop evolve?

Inspired by Google’s MapReduce, which splits an application into small fractions to run on different nodes, scientists Doug Cutting and Mike Cafarella created a platform called Hadoop 1.0 and launched it in the year 2006 to support the distribution of Nutch search engine.

Apache Hadoop was made available for the public in November 2012 by Apache Software Foundation. Named after a yellow soft-toy elephant of Doug Cutting’s kid, this technology has been continuously revised since its launch.

As part of its revision, Apache Software Foundation launched its second revised version Hadoop 2.3.0 on February 20, 2014, with some major changes in the architecture.

 

What is Hadoop streaming?

Hadoop streaming is the generic API that is used for working with streaming data. Both the Mapper and the Reducer obtain their inputs in a standard format. The input is taken from Stdin and the output is sent to Stdout. This is the method within Hadoop for processing a continuous stream of data.

Hadoop is an application that is used for Big Data processing and storing. Hadoop development is the task of computing Big Data through the use of various programming languages such as Java, Scala, and others. Hadoop supports a range of data types such as Boolean, char, array, decimal, string, float, double, and so on.

Why should we use Apache Hadoop?

With evolving big data around the world, the demand for Hadoop Developers is increasing at a rapid pace. Well-versed Hadoop Developers with the knowledge of practical implementation are very much required to add value into the existing process. However, apart from many other reasons, the following are the main reasons to use this technology:

·         Extensive use of Big Data: More and more companies are realizing that in order to cope with the outburst of data, they will have to implement a technology that could subsume such data into itself and come out with something meaningful and valuable. Hadoop has certainly addressed this concern, and companies are tending toward adopting this technology. Moreover, a survey conducted by Tableau reports that among 2,200 customers, about 76 percent of them who are already using Hadoop wish to use it in newer ways.

·         Customers expect security: Nowadays, security has become one of the major aspects of IT infrastructure. Hence, companies are keenly investing in the security elements more than anything. Apache Sentry, for instance, enables role-based authorization to the data stored in the Big Data cluster.

·         Latest technologies taking charge: The trend of Big Data is going upward as users are demanding higher speed and thus are rejecting the old school data warehouses. Realizing the concern of its customers, Hadoop is actively integrating the latest technologies such as Cloudera Impala, AtScale, Actian Vector, Jethro, etc. in its basic infrastructure.

 

Why do we need Hadoop?

The explosion of big data has forced companies to use the technologies that could help them manage the complex and unstructured data in such a way that maximum information could be extracted and analyzed without any loss and delay. This necessity sprouted the development of Big Data technologies that are able to process multiple operations at once without a failure.

Some of the features of Hadoop are as listed below:

·         Capable of storing and processing complex datasets: With increasing volumes of data, there is a greater possibility of data loss and failure. However, Hadoop’s ability to store and process large and complex unstructured datasets makes it somewhat special.

·         Great computational ability: Its the distributed computational model enables fast processing of big data with multiple nodes running in parallel.

·         Lesser faults: Implementing it leads to a lesser number of failures as the jobs are automatically redirected to other nodes as and when one node fails. This ultimately causes the system to respond in real-time without failures.

·         No preprocessing required: Enormous data can be stored and retrieved at once, including both structured and unstructured data, without having to preprocess them before storing into the database.

·         Highly scalable: It is a highly scalable Big Data tool as you can raise the size of the cluster from a single machine to thousands of servers without having to administer extensively.

·         Cost-effective: Open-source technologies come free of cost and hence require a lesser amount of money for implementing them.