Latest Update made on November 29,2016
Is Hadoop easy to learn?
For most professionals who are from various backgrounds like - Java, PHP, .net, mainframes, data warehousing, DBAs, data analytics - and want to get into a career in Hadoop and Big Data, this is the first question they ask themselves and their peers. It is an obvious question - you want to invest your time and money to learn Hadoop, a new technology, but you also need to understand if it will be worth your time, if you can understand how to work on Hadoop, as easily as you work on the technologies, you are currently an expert in. Recent graduates, with no work experience on other technologies will find it very difficult to get hired as Hadoop developers. Indeed, most companies absolutely insist on hiring only experienced professionals. There are several reasons for that - the first one being - Hadoop is not an easy technology to master.
Learning Hadoop is not an easy task but it becomes hassle-free if students know about the hurdles overpowering it. One of the most frequently asked questions by prospective Hadoopers is- “How much java is required for hadoop”? Hadoop is an open source software built on Java thus making it necessary for every Hadooper to be well-versed with at least java essentials for hadoop. Having knowledge of advanced Java concepts for hadoop is a plus but definitely not compulsory to learn hadoop. Your search for the question “How much Java is required for Hadoop?” ends here as this article explains elaborately on java essentials for Hadoop.
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How much Java is required for Hadoop?
Apache Hadoop is one of the most commonly adopted enterprise solution by big IT giants making it one of the top 10 IT job trends for 2015. Thus, it is mandatory for intelligent technologists to pick up Hadoop quickly with Hadoop ecosystem getting bigger day by day. The outpouring demand for big data analytics is landing many IT professionals to switch their careers to Hadoop technology. Professionals need to consider the skills before they begin to learn Hadoop.
Skills to Learn Hadoop-
Java Knowledge Required for Hadoop-
Hadoop is written in Java, thus knowledge of Java basics is essential to learn Hadoop.
Learn Hadoop to become a Microsoft Certified Big Data Engineer.
Linux Knowledge Required for Hadoop-
Hadoop runs on Linux, thus knowing some basic Linux commands will take you long way in pursuing successful career in Hadoop.
According to Dice, Java-Hadoop combined skill is in great demand in the IT industry with increasing Hadoop jobs.
Career counsellors at DeZyre frequently answer the question posed by many of the prospective students or professionals who want to switch their career to big data or Hadoop- “How much Java is required for Hadoop”?
Most of the prospective students exhibit some kind of disappointment when they ask this question –they feel not knowing Java to be a limitation and they might have to miss on a great career opportunity. It is one of the biggest myth that- a person from any other programming background other than Java cannot learn Hadoop. (Click here to Tweet)
There are several organizations who are adopting Apache Hadoop as an enterprise solution with changing business requirements and demands. The demand for Hadoop professionals in the market is varying remarkably. Professionals with any of the diversified tech skills like – Mainframes, Java, .NET , PHP or any other programming language expert can learn Hadoop.
If an organization runs an application built on mainframes then they might be looking for candidates who possess Mainframe +Hadoop skills whereas an organization that has its main application built on Java would demand a Hadoop professional with expertise in Java+Hadoop skills.
Let’s consider this analogy with an example-
The below image shows a job posting on Monster.com for the designation of a Senior Data Engineer-
The job description clearly states that any candidate who knows Hadoop and has strong experience in ETL Informatica can apply for this job to build a career in Hadoop technology without expertise knowledge in Java.The mandatory skills for the job have been highlighted in red which include Hadoop, Informatica,Vertica, Netezza, SQL, Pig, Hive. The skill MapReduce in Java is an additional plus but not required.
Here is another image which shows a job posting on Dice.com for the designation of a Big Data Engineer-
The job description clearly underlines the minimum required skills for this role as Java, Linux and Hadoop. Candidates who have expertise knowledge in Java, Linux and Hadoop can only apply for this job and anybody with Java basics would not be the best fit for this job.
Some of the job roles require the professional to have explicit in-depth knowledge of Java programming whereas few other job roles can be excelled even by professionals who are well-versed with Java basics.
To learn Hadoop and build an excellent career in Hadoop, having basic knowledge of Linux and knowing the basic programming principles of Java is a must. Thus, to incredibly excel in the entrenched technology of Apache Hadoop, it is recommended that you at least learn Java basics.
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Java and Linux- Building Blocks of Hadoop
Apache Hadoop is an open source platform built on two technologies Linux operating system and Java programming language. Java is used for storing, analysing and processing large data sets. The choice of using Java as the programming language for the development of hadoop is merely accidental and not thoughtful. Apache Hadoop was initially a sub project of the open search engine Nutch. The Nutch team at that point of time was more comfortable in using Java rather than any other programming language. The choice for using Java for hadoop development was definitely a right decision made by the team with several Java intellects available in the market. Hadoop is Java-based, so it typically requires professionals to learn Java for Hadoop.
Apache Hadoop solves big data processing challenges using distributed parallel processing in a novel way. Apache Hadoop architecture mainly consists of two components-
1.Hadoop Distributed File System (HDFS) –A virtual file system
2.Hadoop Java MapReduce Programming Model Component- Java based system tool
HDFS is the virtual file system component of Hadoop that splits a huge data file into smaller files to be processed by different processors. These small files are then replicated and stored on various servers for fault tolerance constraints. HDFS is a basic file system abstraction where the user need not bother on how it operates or stores files unless he/she is an administrator.
Google’s Java MapReduce framework is the roost of large scale data processing( YARN can also be used for data processing with Hadoop 2.0).Hadoop Java MapReduce component is used to work with processing of huge data sets rather than bogging down its users with the distributed environment complexities.
The Map function mainly filters and sorts data whereas Reduce deals with integrating the outcomes of the map () function. Google’s Java MapReduce framework provides the users with a java based programming interface to facilitate interaction between the Hadoop components. There are various high level abstraction tools like Pig (programmed in Pig Latin ) and Hive (programmed using HiveQL) provided by Apache to work with the data sets on your cluster. The programs written using either of these languages are converted to MapReduce programs in Java.The MapReduce programs can also be written in various other scripting languages like Perl, Ruby, C or Python that support streaming through the Hadoop streaming API, however, there are certain advanced features that are as of now available only with Java API.
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At times, Hadoop developers might be required to dig deep into Hadoop code to understand the functionality of certain modules or why a particular piece of code is behaving strange. Under, such circumstances knowledge of Java basics and advanced programming concepts comes as a boon to Hadoop developers. Technology experts’ advice prospective Hadoopers to learn Java basics before they deep dive into Hadoop for a well-rounded real world Hadoop implementation. Career counsellors suggest students to learn Java for Hadoop before they attempt to work on Hadoop Map Reduce.
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How to learn Java for Hadoop?
If you are planning to enrol for Hadoop training, ramp up java knowledge required for hadoop beforehand.
- Professionals aspiring to pursue a successful career in Hadoop can try to learn Java on their own by reading various e-books or by checking out free Java tutorials available online. The learning approach through Java tutorials will work out if a person is skilled at programming. Java tutorials will help you understand and retain information with practical code snippets. This approach might not be the best choice for less experienced programmers as they might not be able to comprehend the code snippets and other examples in the Java tutorial with ease.
- There are several reputed online e-learning classes which provide great options to learn Java for Hadoop. Knowledge experts explain Java basics, plus the students can clarify any doubts they have then and there and engage in discussion with other students to improve their java concepts for hadoop.
Candidates who enrol for DeZyre’s IBM certified Hadoop training can activate a free java course to ramp up their java knowledge required for hadoop. Individuals who are new to Java can also get started to learn Hadoop just by understanding the Java essentials for hadoop taught as part of the free java course curriculum at DeZyre. DeZyre’s 20 hours Java Course curriculum covers all the Java essentials for hadoop, such as-
Installing and Configuring Java and Eclipse -
To learn Java for Hadoop, you will first need to install Eclipse and Java.
Eclipse is an Integrated Development Environment (IDE) which is used for building applications in languages like Java, C, C++, C#, etc. It is built from ground-up just to facilitate other languages. Eclipse does not have a great design for end-use functionality by itself. It is designed to provide a robust integration with each Operating System and has a common user interface model. The Eclipse platform is composed of plug-ins. For example, the JDT - Java Development Tools project allows Eclipse to be used as Java IDE.
System Requirements for Installing Java: Now that you know, that learning Java for Hadoop will help you in gaining expertise in this new technology, let us get started from the beginning. Since Eclipse and Java can be integrated in any OS, let us understand what are the system requirements to install Java:
Java for Windows : Windows 7, Windows 8 or Windows 10; 64-bit OS, 128 MB RAM, Disk Space should be 124MB for JRE and 2MB for Java Update. Minimum requirement for processor should be Pentium 2 266MHz. You have to use these browsers - Internet Explorer 9 and above or Firefox.
Java for Mac OS X : Your system should be an Intel based Mac running Mac OS X 10.8.3+ or 10.9+. You need to have administrator privilege for installation and a 64-bit browser, either Safari or Firefox.
These requirements are which Java 8 supports.
Arrays - Arrays are container type objects, or a data structure in Java, that holds a fixed number of elements of a single type. Or like you studied in Math - you can define Array as a collection of variables of one type. When creating an Array, the length of the Array is fixed. Each item or variable in the Array is called an ‘element’. Arrays is a very powerful concept used in programming. Since the goal is to analyse data, arrays provide a good base on large data can be broken and categorized with assigned values.
Get Started with Arrays in Java through this "Learn Java for Hadoop Tutorial:Arrays"
Objects and Classes - Java is an Object Oriented programming Language, where any program is designed using objects and classes. An Object is defined as a physical as well as logical entity, whereas a Class is just a logical entity. For example - any object that we see around us will have a state, a behaviour and an identity. A Class can be defined as a template on which describes the type of the object, the state and the behaviour of it. A group of Objects having common properties will constitute a class.
Get Started with Classes and Objects in Java through this "Learn Java for Hadoop Tutorial:Classes and Objects"
Control Flow Statements - In Java, the statements inside any source file are executed in an ascending order, i.e from top to bottom. Control flow statements are commands that allow breaks in the execution pattern. You can actually customize and execute particular blocks of code in your source file - using control flow statements.
If-then-else statement is the most basic and popular control flow statement. If you want a particular block of code to be executed only If - certain conditions are ‘true’, then the If-then-else clause will return the value ‘false’, once the condition is not met.
These statements in Java are crucial for data analysis and for writing MapReduce jobs suitable for conditional big data analysis.
Interfaces and Inheritance - An interface is a platform which allows different systems or programs to interact with each other. Similar to say a person interacting with a computer - where we type in commands or instruction for the computer by way of the keyboard. Here, the keyboard is an interface. Similarly, in programming, it is necessary that different groups of programmers should be able to write a code which is understandable by disparate groups without specific instructions. Programmers need to have a contract that lays out the rules of software interaction.
Interfaces are such “contracts” which allows each group of programmers to write their code even if they do not know how the other group is writing its code. In a software programming language - interface is a service contract between a library that has the services and the code that calls the services to be implemented.
For example, let’s say the programmer wants to call the I/O service - the Java program will obtain the I/O (input/output) services by creating objects and classes from the Java class library and calling the methods. These classes and methods are known as interfaces. Interfaces are reference types and contain constants, default methods, static methods, method signatures and nested types.
Every class in Java has a superclass or a subclass - this is because in Java - each class can be derived from another class. In doing so - the derived class retains the properties, method, fields of the other superclass or the base class. This is known as inheritance which allows information to be stored in a hierarchical order.
The concept of inheritance is simple yet it is very useful. Say you want to create a new class, but you know that there is an existing class library in Java that already has some properties, methods and code that you need.
Get Started with understanding the concept of Inheritance and implemntation of interfaces in Java through this "Learn Java for Hadoop Tutorial:Inheritance and Interfaces"
The mechanism to handle runtime malfunctions is referred to as Exception Handling. The block of java code that handles the exception is known as Exception Handler. When there is an Exception, the flow of the program is disturbed or abruptly terminated. Exceptions occur due to various reasons- hardware failures, programmer error,a file that needs to be opened cannot be found, resource exhaustion etc.
Throwable class is considered to be on the top in the classification of exceptions.
There are three types of Exceptions which come under it -
Checked Exception: These kind of exceptions can be predicted and resolved by the programmer.
This is something that the programmer will be aware of. It will be checked during compile time.
Unchecked Exception: This class is the extension of RuntimeException.This type of exception is checked at the runtime and ignored during the compile time.
Error: Errors cannot be recovered and neither can be handled in the program code.The only solution to exit from errors is to terminate execution of the program.
Serialization is a mechanism in which an object is represented as a sequence or stream of bytes.The stream of bytes contains information about the type of the object and the kind of data stored in it. The type of information and bytes that represent the object and its data can be used to recreate the object in memory and this process is the reverse process of serialization known as deserialization. The whole process is JVM independent. An object can serialized in one platform and can be deserialized in a completely different platform.
Two classes which contain methods for serializing and deserializing an object.
ObjectInputStream class deserializes objects and primitive data types that have been serialized using ObjectOutputStream.
An object that groups multiple elements into a single unit is called a Collection. A collection object in java holds references to other objects. It is used to store, retrieve, manipulate, and communicate aggregate data.
All collections frameworks contain the following:
1) Interfaces : These are abstract data types that represent collections. Interfaces usually form a hierarchy in object- oriented languages. Collections can be manipulated independently irrespective of their representations. Interfaces include Set, List, Queue, SortedSet, Enumeration, Map, Map.Entry, Deque etc.
2) Implementations : Implementations in Java are concrete implementations of classes i.e. they are reusable data structures. Commonly used Implementations include ArrayList, Vector, LinkedList, PriorityQueue, HashSet, LinkedHashSet, TreeSet etc.
3) Algorithms: Computations like searching and sorting of data on objects which implement collection interfaces are performed using Algorithms. Algorithms are polymorphic in nature i.e. programmers can use the same method with different implementations for a particular collections interface.
Spending few hours on Java basics will act as a great catalyst to learn Hadoop.
If you are interested in becoming a Hadoop developer, but you are concerned about mastering Java concepts for Hadoop, then you can talk to one of our career counsellors. Please send an email to email@example.com
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Want to make a career in big data? Learn Java Essentials for Hadoop.