Hadoop Developer Course with MapReduce and Java

BY
Udemy

Gain certification as a Hadoop developer by mastering the principles and methodologies associated with MapReduce and Java.

Mode

Online

Fees

₹ 799

Quick Facts

particular details
Medium of instructions English
Mode of learning Self study
Mode of Delivery Video and Text Based

Course overview

A fully accessible framework called Hadoop has been used to store and organize big data applications that are operated inside cluster configurations. Hadoop developer develops applications to handle and administer big data for an organization. Hadoop Developer Course with MapReduce and Java certification is developed by Inflame Tech and is made available by Udemy.

Hadoop Developer Course with MapReduce and Java online classes are geared toward assisting candidates in understanding MapReduce programming, how to establish in an appropriate environment, and how to publish and operate MapReduce programs. Hadoop Developer Course with MapReduce and Java online training provides 8 hours of comprehensive lessons which aim to help candidates master the abilities needed to transform large amounts of data into useful information, process that data in parallel on the Hadoop cluster, and make it accessible to users.

The highlights

  • Certificate of completion
  • Self-paced course
  • 8 hours of pre-recorded video content
  • 1 article
  • 3 downloadable resources

Program offerings

  • Online course
  • Downloadable learning resources
  • 30-day money-back guarantee
  • Unlimited access
  • Accessible on mobile devices and tv

Course and certificate fees

Fees information
₹ 799
certificate availability

Yes

certificate providing authority

Udemy

What you will learn

Java Knowledge of big data

After completing the Hadoop Developer Course with MapReduce and Java online certification, candidates will obtain a comprehensive understanding of the principles associated with big data. Candidates will explore the capabilities of Java, MapReduce, Hadoop, and YARN. Candidates will gain knowledge of serialization, deserialization, dataflow, cluster modes, HDFS, and Hadoop clusters. Additionally, candidates will learn how to use features like the job tracker, task tracker, reducer, mapper, and deriver class.

The syllabus

Module-1 Introduction to Course

  • Introduction
  • Prerequisites
  • what you will learn
  • Need of MapReduce

Module-2 A Look at Hadoop

  • What is Hadoop
  • Hadoop History
  • Comparison of HDFS with RDBMS
  • Hadoop Cluster
  • Hadoop Features
  • Cluster Modes in Hadoop
  • Hadoop Core Components
  • What is HDFS
  • Block Replication in HDFS
  • HDFS and MapReduce
  • HDFS Daemons

Module-3 MapReduce Basics

  • What is MapReduce
  • Why MapReduce
  • History of MapReduce
  • Use Cases to Illustrate Advantages of MapReduce
  • MapReduce Applications
  • Anatomy of MapReduce Program
  • Map and Reduce Function
  • Hands-On Session

Module-4 Understanding MapReduce

  • Dataflow in MapReduce
  • Job Submission Flow of MapReduce
  • MapReduce Example
  • MapReduce Daemons
  • Job Tracker
  • Task Tracker
  • Task Assignment by JobTracker
  • Submission of MapReduce Job
  • Hands-On
  • Combiner and partitioner
  • Dataflow with a Single, Multiple and No Reduce Task

Module-5 MapReduce with YARN

  • Hadoop 1.x Architecture
  • Hadoop 1.x Problems
  • NameNode-No Horizontal Scalability
  • No High Availability in NameNode
  • JobTracker-Overburdened
  • MRv1
  • Hadoop 2.x New Features
  • Hadoop 2.x Architecture
  • HDFS High Availability in Hadoop 2.x Architecture
  • YARN-Moving Beyond MapReduce
  • Different Processing Applications in YARN
  • MRv2 (YARN)
  • YARN MR Application Execution Flow
  • YARN Workflow
  • MapReduce 2.x Cluster Architecture
  • Hands-On

Module-6 Advanced MapReduce Concepts - I

  • InputSplit and RecordReader
  • Mapper, Reducer and Driver Class
  • New vs Old API
  • Generic Option Parser, Tool and ToolRunner
  • GenericOptionsParser and ToolRunner Options
  • Writables in Hadoop
  • Serialization and Deserialization
  • Chaining of Jobs
  • Listing and Killing Jobs
  • Distributed Cache
  • Counters
  • Test cases in Hadoop

Module-7 Advance Mapreduce Concepts-II

  • Schedulers
  • Implement Fair Scheduler in CDH
  • Data Compression in Hadoop
  • Different Compression Techniques in Hadoop
  • Hands-On
  • Multiple Inputs
  • Tuning
  • Profiling Map and Reduce Task
  • Filtering and Projection in Map Phase
  • Use Combiner Class
  • Analyze XML data using Map Reduce Framework
  • Custom Partitioner in Map Reduce

Module-8 Advance Mapreduce Concepts-III

  • Joining in MapReduce
  • Different Input and Output Formats in MapReduce

Program & Projects for full Course

  • Link for all Projects

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