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Big Data And Hadoop Job Roles And Opportunities

Big data hadoop training Courses

Why Enroll for Big Data and Hadoop at IIHT?

Whenever there is a discussion on Big Data, it is very unlikely that Hadoop doesn’t come into the picture. Though Big Data is a broader concept than Hadoop, Hadoop has become such an integral part of Big Data that many a times both these concepts are used synonymously. Interestingly, at IIHT, students not only learn Hadoop (HDFS and MapReduce) but also other tools related to Big Data such as Spark, Hive, Pig and Latin, HBase, Sqoop, YARN and Hadoop Security.

Don’t know Java? Not to worry at all! IIHT’s engineering programme in Big Data and Hadoop begins with the fundamentals of Java that will equip with the required skills to understand Hadoop concepts with ease!

To your surprise, Hadoop skills are one of the top skills in demand in 2016 and in coming years. Many companies are looking out for professionals who can demonstrate Hadoop skills as well as related Technologies included in our programme.

If career progression is on your mind, IIHT’s high-end Big Data and Hadoop programme is the ideal choice for you as well as for employers!

Module 1

Introduction to Java Fundamentals

  • Basic java concepts
  • Multi-threading
  • File I/O –Java. IO
  • Collections –Java.Util.*, Java.Math, Java.Lang
  • Java Generics
  • Java Serialization
  • Java Database Connectivity –JDBC
  • Java Common Design Patterns
  • Java Open Source Frameworks (Spring, Apache Maven, Logging, etc…)
  • Java Apache Hadoop Frameworks (Hadoop Common, Map Reduce etc.)
  • Understand Web Servers & Application Servers – JBoss Application server, Apache Tomcat server
  • Java Unit testing Frameworks (Junit / TestNG)
  • Eclipse IDE – Java Development.
  • Version Control – GIT, SVN, etc.
  • Java Continuous Integration frameworks – Husdson, Jenkins, etc.
  • Handling XML and XSD using Java frameworks
  • Java XML Parsers frameworks – DOM and SAX
  • Java Web services concepts – SOA, SOAP, XML, JAXB.
  • SOAP Web services
  • REST web services

Hadoop Fundamentals

  • What is Big Data? Why Big Data?
  • Hadoop Architecture & Components
  • Hadoop Storage & File Formats (ASCII, Avro, Parquet, RC4, JSON, EBCDIC etc.)
  • Hadoop Processing – Map Reduce, Spark Frameworks

HDFS

  • HDFS Basics
  • File Storage
  • Fault Tolerance

MapReduce

  • What Is MapReduce?
  • Basic MapReduce Concepts
  • Concepts of Mappers, Reducers, Combiners and Paritioning
  • Inputs and Output formats to MR Program
  • Error Handling and creating UDFs for MR

Spark

  • What Is Spark?
  • Basic Spark Concepts
  • How Spark differs from Map Reduce?
  • Working with RDD’s
  • Parallel Programming with Spark
  • Spark Streaming

Hive

  • What is Hive, why we need it and its importance in DWH?
  • How Hive is different from Traditional RDBMS
  • Modeling in Hive, creating Hive structures and data load process.
  • Concepts of Partitioning, Bucketing, Blocks, Hashing, External Tables etc.
  • Concepts of serialization, deserialization
  • Different Hive data storage formats including ORC, RC, and Parquet.
  • Introduction ton HiveQL and examples.
  • Hive as an ELT tool and difference between Pig and Hive
  • Performance tuning opportunities in Hive, learnings and Best Practices.
  • Writing and mastering Hive UDFs

Pig and Latin

  • Basics of Pig and Why Pig?
  • Grunt
  • Pig’s Data Model
  • Writing Evaluation
  • Filter
  • Load & Store Functions
  • Benefits of Pig over SQL language
  • Input and Output formats to MR program.
  • Error Handling and scope of creating UDFs for Pig

HBase

  • HBase – Introduction
  • When to use HBase
  • HBase Data Model
  • HBase Families & Components
  • Data Storage and Distribution
  • HBase Master

Sqoop

  • Sqoop Overview
  • Sqoop Exercises

YARN

  • YARN Overview
  • HDFS 2

MongoDB

  • Introduction to In-Memory Computing
  • When to use MongoDB
  • MongoDB API
  • Indexing and Data Modeling
  • Drivers / Replication / Sharding

Hadoop Security

  • Security Overview
  • Knox Exercise
  • Access Control Labels

Big Data And Hadoop Job Roles

Each module in Big Data And Hadoop is well-designed that make you eligible for certain job roles as mentioned below:

  • Hadoop Developer
  • Hadoop Consultant
  • Technical Lead – Big Data
  • Hadoop Engineer
  • Senior Hadoop Engineer
  • Computer Scientist – Hadoop Developer
  • Analytics – Tech Lead
  • MAP Reduce Application Developer
  • Hadoop Administrator
  • Custom Hadoop Application Developer
  • Business Analyst

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