Advantages of Hadoop

Advantages of Hadoop

• Hadoop framework allows the user to quickly write and test distributed systems. It is efficient, and it automatic distributes the data and work across the machines and in turn, utilizes the underlying parallelism of the CPU cores.
• Hadoop does not rely on hardware to provide fault-tolerance and high availability (FTHA), rather Hadoop library itself has been designed to detect and handle failures at the application layer.
• Servers can be added or removed from the cluster dynamically and Hadoop continues to operate without interruption.
• Another big advantage of Hadoop is that apart from being open source, it is compatible on all the platforms since it is Java based.

How Does Hadoop Work?

Stage 1

A user/application can submit a job to the Hadoop (a hadoop job client) for required process by specifying the following items:

1. The location of the input and output files in the distributed file system.
2. The java classes in the form of jar file containing the implementation of map and reduce functions.
3. The job configuration by setting different parameters specific to the job.

Stage 2

The Hadoop job client then submits the job (jar/executable etc) and configuration to the JobTracker which then assumes the responsibility of distributing the software/configuration to the slaves, scheduling tasks and monitoring them, providing status and diagnostic information to the job-client.

Stage 3

The TaskTrackers on different nodes execute the task as per MapReduce implementation and output of the reduce function is stored into the output files on the file system.

Hadoop Distributed File System

Hadoop can work directly with any mountable distributed file system such as Local FS, HFTP FS, S3 FS, and others, but the most common file system used by Hadoop is the Hadoop Distributed File System (HDFS).

The Hadoop Distributed File System (HDFS) is based on the Google File System (GFS) and provides a distributed file system that is designed to run on large clusters (thousands of computers) of small computer machines in a reliable, fault-tolerant manner.

HDFS uses a master/slave architecture where master consists of a single NameNode that manages the file system metadata and one or more slave DataNodes that store the actual data.

A file in an HDFS namespace is split into several blocks and those blocks are stored in a set of DataNodes. The NameNode determines the mapping of blocks to the DataNodes. The DataNodes takes care of read and write operation with the file system. They also take care of block creation, deletion and replication based on instruction given by NameNode.

HDFS provides a shell like any other file system and a list of commands are available to interact with the file system. These shell commands will be covered in a separate chapter along with appropriate examples.

MapReduce

Hadoop MapReduce is a software framework for easily writing applications which process big amounts of data in-parallel on large clusters (thousands of nodes) of commodity hardware in a reliable, fault-tolerant manner.
The term MapReduce actually refers to the following two different tasks that Hadoop programs perform:

• The Map Task: This is the first task, which takes input data and converts it into a set of data, where individual elements are broken down into tuples (key/value pairs).
• The Reduce Task: This task takes the output from a map task as input and combines those data tuples into a smaller set of tuples. The reduce task is always performed after the map task.

Typically both the input and the output are stored in a file-system. The framework takes care of scheduling tasks, monitoring them and re-executes the failed tasks.

The MapReduce framework consists of a single master JobTracker and one slave TaskTracker per cluster-node. The master is responsible for resource management, tracking resource consumption/availability and scheduling the jobs component tasks on the slaves, monitoring them and re-executing the failed tasks. The slaves TaskTracker execute the tasks as directed by the master and provide task-status information to the master periodically.

The JobTracker is a single point of failure for the Hadoop MapReduce service which means if JobTracker goes down, all running jobs are halted.

Hadoop Architecture

Hadoop framework includes following four modules:

• Hadoop Common: These are Java libraries and utilities required by other Hadoop modules. These libraries provides filesystem and OS level abstractions and contains the necessary Java files and scripts required to start Hadoop.
• Hadoop YARN: This is a framework for job scheduling and cluster resource management.
• Hadoop Distributed File System (HDFS™): A distributed file system that provides high-throughput access to application data.
• Hadoop MapReduce: This is YARN-based system for parallel processing of large data sets.

We can use following diagram to depict these four components available in Hadoop framework.

Since 2012, the term "Hadoop" often refers not just to the base modules mentioned above but also to the collection of additional software packages that can be installed on top of or alongside Hadoop, such as Apache Pig, Apache Hive, Apache HBase, Apache Spark etc.
 

MapReduce

Hadoop Distributed File System

Hadoop - Introduction

Hadoop is an Apache open source framework written in java that allows distributed processing of large datasets across clusters of computers using simple programming models. 

A Hadoop frame-worked application works in an environment that provides distributed storage and computation across clusters of computers. Hadoop is designed to scale up from single server to thousands of machines, each offering local computation and storage.

History of Hadoop

Doug Cutting, Mike Cafarella and team took the solution provided by Google and started an Open Source Project called HADOOP in 2005 and Doug named it after his son's toy elephant. Now Apache Hadoop is a registered trademark of the Apache Software Foundation. 

Hadoop runs applications using the MapReduce algorithm, where the data is processed in parallel on different CPU nodes. In short, Hadoop framework is capable enough to develop applications capable of running on clusters of computers and they could perform complete statistical analysis for a huge amounts of data.

Hadoop - Big Data Solutions

Traditional Approach

In this approach, an enterprise will have a computer to store and process big data. Here data will be stored in an RDBMS like Oracle Database, MS SQL Server or DB2 and sophisticated softwares can be written to interact with the database, process the required data and present it to the users for analysis purpose.

Limitation

This approach works well where we have less volume of data that can be accommodated by standard database servers, or up to the limit of the processor which is processing the data. But when it comes to dealing with huge amounts of data, it is really a tedious task to process such data through a traditional database server.

Google’s Solution

Google solved this problem using an algorithm called MapReduce. This algorithm divides the task into small parts and assigns those parts to many computers connected over the network, and collects the results to form the final result dataset.

Above diagram shows various commodity hardwares which could be single CPU machines or servers with higher capacity.

Big Data Challenges

The major challenges associated with big data are as follows:

• Capturing data
• Curation
• Storage
• Searching
• Sharing
• Transfer
• Analysis
• Presentation

To fulfill the above challenges, organizations normally take the help of enterprise servers.

Operational vs. Analytical Systems

	Operational	Analytical
Latency	1 ms - 100 ms	1 min - 100 min
Concurrency	1000 - 100,000	1 - 10
Access Pattern	Writes and Reads	Reads
Queries	Selective	Unselective
Data Scope	Operational	Retrospective
End User	Customer	Data Scientist
Technology	NoSQL	MapReduce, MPP Database

Analytical Big Data

This includes systems like Massively Parallel Processing (MPP) database systems and MapReduce that provide analytical capabilities for retrospective and complex analysis that may touch most or all of the data.
MapReduce provides a new method of analyzing data that is complementary to the capabilities provided by SQL, and a system based on MapReduce that can be scaled up from single servers to thousands of high and low end machines.
These two classes of technology are complementary and frequently deployed together.

Operational Big Data

This include systems like MongoDB that provide operational capabilities for real-time, interactive workloads where data is primarily captured and stored.

NoSQL Big Data systems are designed to take advantage of new cloud computing architectures that have emerged over the past decade to allow massive computations to be run inexpensively and efficiently. This makes operational big data workloads much easier to manage, cheaper, and faster to implement.

Some NoSQL systems can provide insights into patterns and trends based on real-time data with minimal coding and without the need for data scientists and additional infrastructure.

Big Data Technologies

Big data technologies are important in providing more accurate analysis, which may lead to more concrete decision-making resulting in greater operational efficiencies, cost reductions, and reduced risks for the business.

To harness the power of big data, you would require an infrastructure that can manage and process huge volumes of structured and unstructured data in realtime and can protect data privacy and security.

There are various technologies in the market from different vendors including Amazon, IBM, Microsoft, etc., to handle big data. While looking into the technologies that handle big data, we examine the following two classes of technology:

Benefits of Big Data

Big data is really critical to our life and its emerging as one of the most important technologies in modern world. Follow are just few benefits which are very much known to all of us:

• Using the information kept in the social network like Facebook, the marketing agencies are learning about the response for their campaigns, promotions, and other advertising mediums.
• Using the information in the social media like preferences and product perception of their consumers, product companies and retail organizations are planning their production.
• Using the data regarding the previous medical history of patients, hospitals are providing better and quick service.
  

What Comes Under Big Data?

Big data involves the data produced by different devices and applications. Given below are some of the fields that come under the umbrella of Big Data.

• Black Box Data : It is a component of helicopter, airplanes, and jets, etc. It captures voices of the flight crew, recordings of microphones and earphones, and the performance information of the aircraft.
• Social Media Data : Social media such as Facebook and Twitter hold information and the views posted by millions of people across the globe.
• Stock Exchange Data : The stock exchange data holds information about the ‘buy’ and ‘sell’ decisions made on a share of different companies made by the customers.
• Power Grid Data : The power grid data holds information consumed by a particular node with respect to a base station.
• Transport Data : Transport data includes model, capacity, distance and availability of a vehicle.
• Search Engine Data : Search engines retrieve lots of data from different databases.
  
  
  
  
  
  Thus Big Data includes huge volume, high velocity, and extensible variety of data. The data in it will be of three types.
  
• Structured data : Relational data.
• Semi Structured data : XML data.
• Unstructured data : Word, PDF, Text, Media Logs.

What is Big Data?

Big data means really a big data, it is a collection of large datasets that cannot be processed using traditional computing techniques. Big data is not merely a data, rather it has become a complete subject, which involves various tools, technqiues and frameworks.

Hadoop - Big Data Overview

Due to the advent of new technologies, devices, and communication means like social networking sites, the amount of data produced by mankind is growing rapidly every year. The amount of data produced by us from the beginning of time till 2003 was 5 billion gigabytes. If you pile up the data in the form of disks it may fill an entire football field. The same amount was created in every two days in 2011, and in every ten minutes in 2013. This rate is still growing enormously. Though all this information produced is meaningful and can be useful when processed, it is being neglected.

90% of the world’s data was generated in the last few years.

Why Java Professionals Should Learn Hadoop

REASONS TO LEARN HADOOP • Hadoop is a Java based framework • Java Professional with Hadoop skills will get More Opportunities • Best career with Hadoop • Best Job Opportunities with Hadoop • Big Data and Hadoop Equal • High Salary With Hadoop than other Technologies • Top Companies Looking for Hadoop Professionals 

JOB ROLES WITH HADOOP • Hadoop Developer • Hadoop Architect • Hadoop Engineer • Hadoop Application Developer • Data Analyst • Data Scientist • Business Intelligence Architect • Big Data Engineer 

DOMAINS LOOKING FOR HADOOP DEVELOPERS: • Besides the obvious IT domain, there are various sectors that require Hadoop Developers. Let’s look at the huge variety of such sectors: • Travel • Retail • Finance • Healthcare • Advertising • Manufacturing • Telecommunications • Life Sciences • Media and Entertainment • Natural Resources • Trade and Transportation • Government • The possible sectors where Hadoop Developers play an important role are limitless. 

JOB RESPONSIBILITIES OF A HADOOP DEVELOPER: A Hadoop Developer has many responsibilities. And the job responsibilities are dependent on your domain/sector, where some of them would be applicable and some might not. The following are the tasks a Hadoop Developer is responsible for: • Hadoop development and implementation. • Loading from disparate data sets. • Pre-processing using Hive and Pig. • Designing, building, installing, configuring and supporting Hadoop. • Translate complex functional and technical requirements into detailed design. • Perform analysis of vast data stores and uncover insights. • Maintain security and data privacy. • Create scalable and high-performance web services for data tracking. • High-speed querying. • Managing and deploying HBase. • Being a part of a POC effort to help build new Hadoop clusters. • Test prototypes and oversee handover to operational teams. • Propose best practices/standards. 

SKILLS REQUIRED FOR HADOOP DEVELOPER • Java Knowledge Required Java basics is essential to learn Hadoop. • Linux Knowledge Required Hadoop runs on Linux, thus knowing some basic Linux commands will take you long way in pursuing successful career in Hadoop. • Good knowledge in back-end programming, specifically java, JS, Node.js and OOAD • Writing high-performance, reliable and maintainable code. • Ability to write MapReduce jobs. • Good knowledge of database structures, theories, principles, and practices. • Ability to write Pig Latin scripts. • Hands on experience in HiveQL. • Familiarity with data loading tools like Flume, Sqoop. • Knowledge of workflow/schedulers like Oozie. • Analytical and problem solving skills, applied to Big Data domain • Proven understanding with Hadoop, HBase, Hive, Pig, and HBase. • Good aptitude in multi-threading and concurrency concepts.

Prerequisites For Hadoop

Before you start proceeding with this tutorial, we assume that you have prior exposure to Core Java, Some Advanced Java Concepts , database concepts, and any of the Linux operating system flavors.


Note : With Out having the knowledge in Core Java and  database concepts Do not learn HADOOP. 

What is Hadoop ?

Hadoop is an open-source framework that allows to store and process big data in a distributed environment across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.

This brief tutorial provides a quick introduction to Big Data, MapReduce algorithm, and Hadoop Distributed File System.