Introduction to Hadoop 

Hadoop is an open-source framework that works for Apache to store processes used to analyze the data. The data volume is high when the data process occurs. Hadoop is an online analytical process. It is written only in Java. It is a process called batch or offline processing. Social platforms like Facebook, Instagram, LinkedIn, Twitter, and other social media use Hadoop.

 

Modules of Hadoop

There are four important modules in Hadoop.

 

 

• HDFS
• Yarn
• Map Reduce
• Hadoop Common

HDFS

The full form of HDFS is Hadoop Distributed File System. HDFS was developed on the basis of GFS when Google published its paper. There are two architecture works in HDFS, one is Single NameNode and the other one is multiple DataNode.  Single NameNode works for matter of role, and DataNode works for the slave of role. To run a commodity both single NameNode and multiple DataNode are eligible. NameNode and DataNode software can be easily run in java language programs. With the help of HDFS, the java language is developed.

Yarn

It is another resource of negotiators; it manages the bundle of data by scheduling jobs. It is one of the frameworks of resource of Hadoop data management.

Map Reduce

By using a key-value, pair data works parallel in computation with the help of java programs where the framework works. The key-value pair data can be computed where the data set converts data input. Reducing the task of consuming, it gives the desired output in the map task.

Hadoop Common

Hadoop and Hadoop modules are used in java libraries. Hadoop commonly supports other Hadoop modules with the collection of utilities. It is one of the important framework modules of Apache.  The other name for Hadoop common is Hadoop core. Hadoop uses all these four modules for data processing.

 

History of Hadoop

In 2002 Apache Nutch was started and it is open-source software. The big data methods were introduced on Apache. This software was devised to get data worth the money and subsequently good results. It became one of the biggest reasons for the emergence of Hadoop.

In 2003 Google introduced GFS (Google File System) to get enough access to data to distributed file systems.

In 2004 Google released a white paper on map reduces. It is a technique and program model for processing works on java based computing. It has some important algorithms on task and map reduction. It converts data and becomes a data set.

In 2005 NDFS was introduced (Nutch distributed file system) by Doug Cutting and Mike Cafarella. It is a new file system in Hadoop. The Hadoop distributed file system and the Nutch distributed file system are the same.

In 2006 Google joined Yahoo with Doug cutting quit. Doug cutting did a new project on Hadoop distributed file system based on Nutch distributed file system. In this same year, Hadoop's first version 0.1.0 was released.

In 2007 yahoo started running two clusters at the same time in 1000 machines.

In 2008 Hadoop became the fastest system.

In 2013 Hadoop 2.2 was released.

In 2017 Hadoop 3.0 was released.

 

What is Hadoop in big data work and its tools?

To implement the storage and processing capacity, cluster processing is used. It is called Hadoop big data. It provides storage for any kind of data and processing power to handle the task. It also helps to build applications for other processes of big data.

Big data has some useful processing data tools and they are:

Apache Hive

A large amount of data is stored in the data warehouse of the Hadoop system.

Apache Zookeeper

In failed NameNode it reduces the failures by automating.

Apache Hbase

It is open-source but not connected with the database in Hadoop.

Apache Flume

It distributes a large amount of data for service.

Apache Sqoop

For Hadoop and relational database, it works as a command-line

Apache Pig

It is the development platform, that helps apache to run on Hadoop. Pig Latin language is used in Apache Pig

Apache Oozie

It manages the Hadoop jobs by scheduling the system to make it easier.

Apache Hcatalog

To sort data from different process tools, the table management tool works in Apache Hcatalog.

 

What is the Hadoop Ecosystem?

Hadoop Ecosystem is the platform that provides services for solving big data problems. Hadoop ecosystem works for Apache projects and other commercial tools to implement and store data.

 

What does Hadoop do and what is Hadoop used for?

Four advantages of Hadoop are discussed below:

Fast: Cluster works in making a map recover the data faster over Hadoop distributed file system. The servers are the same when it works in a data process by using tools. The process makes terabytes in minutes and Petabytes in hours.

Scalable: by adding the nodes to the cluster it gets extended.

Cost-Effective:  traditional relational database management system is more expensive than Hadoop. It is open-source software that can be used for all. And the cost of Hadoop is $1000 a terabyte.

Resilient to failure:  Hadoop distributed file system can replicate data over the network of the property. If one node failure occurs, Hadoop takes the copy of the date to use it.

 

 

What is Hadoop Framework?

Hadoop is an open-source framework of Apache used to store and process a large amount of data for a dataset. Instead of storing large data in a computer, Hadoop helps data to be stored in the computer and in the analysis of it.

Hadoop distributed file system layer works on the storage layer. Hadoop yarn works in the resource management layer and Hadoop map-reduce works in the application layer. To supply input files on a Hadoop-distributed file system, every node map task runs by linking to get output data.

 

Conclusion

In this article, we have discussed what Hadoop is, how it works, its modules, and its advantages. Hadoop is all about handling the process of data. If you want to learn about Hadoop, get in touch with us. Sprintzeal provides popular courses in Big Data and Hadoop. Enroll in Big Data Hadoop Training and get certified. To find the certification that will benefit your career, chat with our course expert and get instant assistance. 

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