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Hadoop Course

Hadoop is developed by Apache Software Foundation which is an open source framework used to process large sets of data such as Big Data and for distributed storage.

HADOOP COURSE & DETAILS

Hadoop Course 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.

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.

Hadoop: Basic Concepts

• An Overview of Hadoop
• The Hadoop Distributed File System
• Hands-On Exercise
• How MapReduce Works
• Hands-On Exercise
• Anatomy of a Hadoop Cluster
• Other Hadoop Ecosystem Components

Writing a MapReduce Program

• The MapReduce Flow
• Examining a Sample MapReduce Program
• Basic MapReduce API Concepts
• The Driver Code
• The Mapper
• The Reducer
• Hadoop’s Streaming API
• Using Eclipse for Rapid Development
• Hands-on exercise
• The New MapReduce API

Common MapReduce Algorithms

• Sorting and Searching
• Indexing
• Machine Learning With Mahout
• Term Frequency – Inverse Document Frequency
• Word Co-Occurrence
• Hands-On Exercise.

PIG

• Data loading in PIG.
• Data Extraction in PIG.
• Data Transformation in PIG.
• Hands on exercise on PIG.

Hive

• Hive Query Language.
• Alter and Delete in Hive.
• Partition in Hive.
• Indexing.
• Joins in Hive.Unions in hive.
• Industry specific configuration of hive parameters.
• Authentication & Authorization.
• Statistics with Hive.
• Archiving in Hive.
• Hands-on exercise

Working with Sqoop

• Introduction.
• Import Data.
• Export Data.
• Sqoop Syntaxs.
• Databases connection.
• Hands-on exercise

Working with Flume

• Introduction.
• Configuration and Setup.
• Flume Sink with example.
• Channel.
• Flume Source with example.
• Complex flume architecture.

OOZIE Concepts
IMPALA Concepts
HUE Concepts
HBASE Concepts
ZooKeeper concepts

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  • India: 040 6678 6677
  • US: 630-636-0198

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Job Preparation

•    Assistance with learning job seeking skills
•    Resume creation
•    Master application completion
•    Dressing for success
•    Job interview preparation

Job Development

•    Assistance with completing applications online or in person
•    Job development online, on foot, networking events, job fairs and established employer relationships to locate available positions in your job goal
•    Job leads and information on attending hiring events
•    Follow-ups on applications placed to request interviews.

What is Hadoop Map Reduce ?

For processing large data sets in parallel across a hadoop cluster, Hadoop MapReduce framework is used.  Data analysis uses a two-step map and reduce process

How Hadoop MapReduce works?

In MapReduce, during the map phase it counts the words in each document, while in the reduce phase it aggregates the data as per the document spanning the entire collection. During the map phase the input data is divided into splits for analysis by map tasks running in parallel across Hadoop framework

Explain what is shuffling in MapReduce ?

The process by which the system performs the sort and transfers the map outputs to the reducer as inputs is known as the shuffle

Explain what is distributed Cache in MapReduce Framework ?

Distributed Cache is an important feature provided by map reduce framework. When you want to share some files across all nodes in Hadoop Cluster, DistributedCache  is used.  The files could be an executable jar files or simple properties file

Explain what is NameNode in Hadoop?

NameNode in Hadoop is the node, where Hadoop stores all the file location information in HDFS (Hadoop Distributed File System).  In other words, NameNode is the centrepiece of an HDFS file system.  It keeps the record of all the files in the file system, and tracks the file data across the cluster or multiple machines

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Course Curriculam

Hadoop Seminar Module

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.

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.

Duration: 45 Days

Support: 24x7

Video: Yes

Hadoop Certified Professional