Hadoop for Hoops: Explore the Whole Ecosystem and to Know How It Really Works

Technological evolution has changed the landscape, everything which we feel and hear today is revolving around some of the modern technology. This technology involves Artificial Intelligence, big data, cloud computing, data science, and much more, which has changed the landscape to a great extent. To integrate this technology, many of the IT professionals are finding and implementing the trajectory of today's modern technologies.

Big Data is one such technology that has gained high momentum during the last few years. And when it comes to big data, then the very first term which strikes in an individual's mind is Hadoop. Not a single big data processing tool has bagged more success and popularity than Hadoop, this open-source tool was launched by Apache Software Foundation on April 1, 2006, and has gained huge popularity among the users in a short duration of time. This field is an ever-evolving field that is evolving continuously, and many features are added to its ecosystem. (Source)

Explore to Know Which Basic Skills You Must Poses For Learning Hadoop

When it comes to learning Hadoop, then one of the basic questions which strike in every individual mind is how they can start learning Hadoop? They do not exactly know which skills they must pose to become a professional with the data handling process. Before any of the individuals start learning Hadoop, they must collect all the basic knowledge related to it in detail. They must get the answer for various questions like,

• Why do they want to learn Hadoop?
• Whether they want to learn it because others are running on the track?
• Will it help them in the long run?

There are many more questions which any of the individuals need to consider, they must also make sure that they evaluate some of the market statistics to find the value of this advanced tool. More than 91% of business entrepreneurs rely on customer data to make perfect decisions for their business. Moreover, they believe that these data can help them in boosting their business success and growth to a great extent.

Marketing strategy is changing continually with the changing trend, there is a surge in all sectors in data generation, it has increased by 90% during the last few decades. Worldwide revenues for business analytics (BDA) and big data solutions are estimated to reach around $189.1 billion this year, this measures an increase of 12.0% from the previous year. A new update from International Data Corporation (IDC) represents that BDA revenues are going to maintain this growth from 2018 to 2022, the compound annual growth rate (CAGR) reach to 13.2%. By 2022, the IDC expects that the BDA revenue is going to reach around $274.3 billion. The gap between the ongoing demand is expected to grow high hence it becomes mandatory to develop the big data skills. Hence, there is an ongoing opportunity that big data has opened for Hadoop professionals, indeed. (Source)

Big Data Introduction

Big Data can be termed as a collection of huge data that has been collected by exploring various platforms. It describes a collection of data that is collected in huge amounts and is growing exponentially with the passage of time. This type of data collection is complex; hence any of the individuals need to make use of the tools which allow them to process and store the data accurately and more efficiently. Big data can be classified into mainly three categories, this category includes:

1. Structured.
2. Unstructured.
3. Semi-structured.

Structured Data

If any of the individuals who want to access, process, and store the data can follow a fixed format, this can be termed as 'structured' data. Computer science has bagged huge success in developing fields over a period of time, hence it becomes much easier for the angularjs development and developers to work with any kind of data, they can even derive value out of it. But in today's world, as the size of data is increasing with each passing day, it becomes quite tougher for any of the individuals to handle the data with this format.

Example of Structured Data:

Unstructured Data

Any data with an unknown structure can be termed as unstructured data. The size of data is comparatively very huge when it comes to unstructured data, it poses multiple challenges and helps in deriving value from the same. A heterogeneous data source is one of the best examples of unstructured data. It simply contains a mixture of simple text, videos, images, and much more.

Example of Unstructured Data:

Semi-Structured

This data type contains both types of data structured and unstructured data. Semi-structured data is organized well in the format; hence it becomes easy for individuals to work with the data more accurately and efficiently than ever before. The XML file is the best example of semi-structured data.

Examples of Semi-structured Data:

As the data is increasing with each passing day and estimated to reach around 78 yottabytes, thus it becomes vital for each and every individual to learn and understand how they can handle the data more accurately and efficiently in a systematic manner. Many tools and techniques are developed, which can help them to work with structured and unstructured data more accurately than ever before.

What Actually Hadoop is?

It is an open-source framework that anyone can use as per their convenience. Any of the individuals can make the needed change in the source code as per their requirement. Apache Hadoop poses the capabilities to process and store huge amounts of data more efficiently. It is used for data analyzing, governance, storing, accessing, processing, and also for performing various actions on the data.

Most of the businesses that have to deal with a huge amount of data on a regular basis make use of Hadoop. It is used to process the large cluster of commodity hardware, which is referred to as a group of a system which is possessing connections with multiple nodes. Hadoop provides numerous features to the users, let's explore to know which are those features that we can leverage when considering using this framework.

Features of Hadoop.

• Scalability.
• Cost-Effective.
• Distribution of Data.
• Parallel Processing of Data.
• Supports Large Clusters of Nodes.
• Automatic Failover Management.
• Supports Heterogeneous Clusters.

The major advantage of Hadoop is scalability, which it offers to the users. It can be used to scale up to more than 200 PB and 4500 server clusters of data. Distributive ways are used to handle stored data, this helps in avoiding the overloading of data in the process.

Hadoop Ecosystem: Explore to Know What it Consists of

The Hadoop ecosystem consists of numerous things which help the users to perform all the tasks more accurately and efficiently than ever before. Let's explore the ecosystem of the Hadoop system to know how it benefits the users in the most accurate way.

Hadoop Distributed File System (HDFS)

HDFS performs the most important role when it comes to using the Hadoop framework. When it comes to storing and distributing data, then it uses nodes that are present in the cluster. The whole process results in reducing the time which is required for storing the data into the disk. Explore the Architecture of HDFS to know how it works to provide systematic output.

MapReduce

Hadoop MapReduce processes the huge amount of data that is stored in a cluster by HDFS. It allows parallel processing on the stored data and resolves the issues via the massive scalability in a cluster.  It provides the high cost of processing standards to the users who choose using it. After collecting the task, the cluster reduces the data to provide users with appropriate results and sends the output back to the Hadoop server.

Code for MapReduce Framework

package hadoop;

import java.util.*;

import java.io.IOException;
import java.io.IOException;

import org.apache.hadoop.fs.Path;
import org.apache.hadoop.conf.*;
import org.apache.hadoop.io.*;
import org.apache.hadoop.mapred.*;
import org.apache.hadoop.util.*;

public class ProcessUnits {
   //Mapper class
   public static class E_EMapper extends MapReduceBase implements
   Mapper<LongWritable ,/*Input key Type */
   Text,            	/*Input value Type*/
   Text,            	/*Output key Type*/
   IntWritable>    	/*Output value Type*/
   {
  	//Map function
  	public void map(LongWritable key, Text value,
  	OutputCollector<Text, IntWritable> output,   
 	 
  	Reporter reporter) throws IOException {
     	String line = value.toString();
     	String last token = null;
     	StringTokenizer s = new StringTokenizer(line,"\t");
     	String year = s.nextToken();
    	 
     	while(s.hasMoreTokens()) {
        	lasttoken = s.nextToken();
     	}
     	int avgprice = Integer.parseInt(last token);
     	output.collect(new Text(year), new IntWritable(avgprice));
  	}
   }
   
   //Reducer class
   public static class E_EReduce extends MapReduceBase implements Reducer< Text, IntWritable, Text, IntWritable > {
   
  	//Reduce function
  	public void reduce( Text key, Iterator <IntWritable> values,
  	OutputCollector<Text, IntWritable> output, Reporter reporter) throws IOException {
     	int max avg = 30;
     	int val = Integer.MIN_VALUE;
       	 
     	while (values.hasNext()) {
        	if((val = values.next().get())>max avg) {
           	output.collect(key, new IntWritable(val));
        	}
     	}
  	}
   }

   //Main function
   public static void main(String args[2])throws Exception {
  	JobConf conf = new JobConf(ProcessUnits.class);
 	 
  	conf.setJobName("max_eletricityunits");
  	conf.setOutputKeyClass(Text.class);
  	conf.setOutputValueClass(IntWritable.class);
  	conf.setMapperClass(E_EMapper.class);
  	conf.setCombinerClass(E_EReduce.class);
  	conf.setReducerClass(E_EReduce.class);
  	conf.setInputFormat(TextInputFormat.class);
  	conf.setOutputFormat(TextOutputFormat.class);
 	 
  	FileInputFormat.setInputPaths(conf, new Path(args[0]));
  	FileOutputFormat.setOutputPath(conf, new Path(args[1]));
 	 
  	JobClient.runJob(conf);
   }
}

Apache Pig

This high-level scripting language is used to write the data analysis programs for a large number of data sets in the Hadoop cluster. Apache Pig allows programmers to enable developers to generate query execution, it allows them to analyze a great amount of data on a regular basis. Pig Latin is the scripting language, which is considered as one of the vital parts of the Pig.

Apache HBase

Apache HBase supports the working of HDFS and is considered to be a column-oriented database that processes on a real-time basis. It performs an effective process on a huge amount of a database, this includes a file containing a large number of columns and rows. Users who aim to manage region servers more accurately can consider using Apache HBase, it helps them to use all the master notes more efficiently than ever before.

Apache Hive

Combo of Apache Hive and SQL-like interface allows users to square the data from HDFS. The new version of the SQL language of Hive is popularly known as HiveQL. Users can install a Hive from a Stable Release, they can start from the initial phase by downloading recent stable releases of Hive from Apache download mirrors. Once after completing the installation process, make sure that you unpack the tarball and create the subdirectory named hive-x.y.z:

$ tar -xzvf hive-x.y.z.tar.gz

After installing the Apache Hive users, let's Set the environment variable to point to the installation directory, consider the simple code to install the directory.

$ cd hive-x.y.z

$ export HIVE_HOME={{pwd}}

Now add $HIVE_HOME to any of the PATH which you desire using the following code:

$ export PATH=$HIVE_HOME/bin:$PATH

Compile Hive on a Master

Once you complete the development task, it's time to compile the same for the master branch, follow the code below to do the task accurately.

Code to Compile Apache Hive

$ git clone https://git-wip-us.apache.org/repos/asf/hive.git
$ cd hive
$ mvn clean package -Pdist [-DskipTests -Dmaven.javadoc.skip=true]
$ cd packaging/target/apache-hive-{version}-SNAPSHOT-bin/apache-hive-{version}-SNAPSHOT-bin
$ ls
LICENSE
NOTICE
README.txt
RELEASE_NOTES.txt
bin/ (all the shell scripts)
lib/ (required jar files)
conf/ (configuration files)
examples/ (sample input and query files)
hcatalog / (hcatalog installation)
scripts / (upgrade scripts for hive-metastore)

Here the version is referred to as the current Hive version. If any of the users is crafting Hive source with the help of Maven (mvn), then it can be referred to the directory <install-dir> for the page. This can help an individual to know whether they have installed the Apache Hive perfectly or not.

Apache Sqoop

Apache Sqoop enables users to import and export, users can transfer any kind and size of data from Hadoop to any other relational database management systems. It also allows them to transfer a large amount of data more quickly than ever before. Apache Sqoop is a connector based architecture that establishes connectivity with all external systems.

Apache Zookeeper

The main role of Apache Zookeeper is to manage the coordination between all the applications or software which are listed above. It looks after the functioning of all the rest software in the Hadoop ecosystem and makes their work much easier than ever before.

Concluding Lines

Learning any technology is the initial journey that leads an individual on the path of success. It opens a huge scope and opportunities for the learners. Hence, motivate and persist yourself to indulge in the world of today's challenging technology. Having a positive learning attitude can help professionals and developers to stay up to date with modern technologies and present market trends. Start your learning journey with Hadoop and leverage various benefits that this advanced framework offers to you.