Big Data HADOOP Online Training

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Enroll in www.imaginelife.in to learn Big data Hadoop training courses and Hadoop certification courses from industry experienced Real time professional through online live classes and E learning courses.

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IT COURSES
Online live classes


www.imaginelife.in
PH:8499068708 :8341832707
EMAIL:[email protected]

Hadoop
Topics To Be covered in Hadoop course
Introduction to Big Data & Hadoop

1.what is Big data?
2.what are the challenges for processing big
data?
3.what is Hadoop?
4.way Hadoop?
5.History of Hadoop
6.Use cases of Hadoop
7.Hadoop eco System
8.HDFS
9.Mapreduce
10.Statistics
Understanding the cluster
1.Typical workflow
2.Writing files to HDPS
3.Reading files from HDFS
4.Rack Awareness
5.5 Daemons
6.HDFS commands HANDS-on
Installing the cluster
1.CDH4 Pseudo cluster
2.CDH4 Multi node cluster
3.configuration
4.cluster on EC2 cloud
5.How to use AWS EMR
6.Hands –on Exercises
Routine Admin and Monitoring Activities
1.Meta Data and Data Backups
2.commissioning and Decommissioning nodes
3.Recover from Namenode Failure
4.Namenode High Availability
5.Monitoring using ganglia and Nagios
Let’s talk MapReduce
1.Before MapReduce
2.MapReduce Overview
3.word count problem
4.word count flow and solution
5.MapReduce flow
6.Algorithms for simple problems
7.Algorithms for complex problems
Developing the MapReduce Application
1.Data types
2.File Formats
3.Explain the Driver,Mapper and Reducer code
4. configuring Development environment-Eclipse
5.Writing Unit Test
6.Running locally
7.Hands –on exercises
How Map Reduce Works
1.Anatomy of map Reduce job run
2.Job Submission
3.Job initialization
4.Task Assignment
5.Job completion

6.Job Scheduling
7.Job Failures
8.shuffle and sort
9.Oozie Workflows
10.Hands –on Exercises
MapReduce Types and Formats
1.MapReduce Types
3.output Formats –text Output, binary output,multiple outputs
4.Lazy output and database output
5.Hands-on Exercises
MapReduce Features
1.Counters
2.Joins-map side and Reduce Side
3.Sorting
4.MapReduce combiner
5.MapReduce partitioner
6.MapReduce Distributed Cache
7.Hands-on Exercises
Hive
1. What is Hive?
2.what Hive is not?
3.Hive Architecture
4.SQL vs Hive QL
5.Data Types
6.Managed Tables and External Tables
7.partitions
8.Buckets

9.Storage formats
10.serDes
11.importing Data
12.Joins-map side and Reduce Side
13. UDFs
14.Hands-on Exercises
Imapla
1.Need for RTQ
2.impala Overview
3.Impala Architecture
4.Hands-on Exercises
Pig
1.What is Pig? Why Pig?
2.Running pig
3.Data
4.pig Latin Statements
5.Schemas
6.Validations
7.Functions and Macros
8.UDFs
9.When to Use pig and HIVE
10.Hands-on Exercises
NoSQL and HBase
1.Why noSQL?
2.Problems with RDBMS
3.cap theorem
4.HBase Concepts
5.Use Cases for HBase
6.HBase Data Model
7.HBase Shell
8.HBase Architecture
9.Minor & major Compaction
10.Bloom Filter&Block cache
11.Schema Design
12.Hands-0n Exercises
Sqoop
1.What is Sqoop?
2.Motivation
3.Sqoop Commands
4.Importing Data to
HDFS
HIVE
HBase
5.Exposing Data
6.Sqoop Connectors
7.Hands on Exercises
FLUME
1.What is Flume?
2.Use Cases
3.Flume Topology –Source,Channel and Sink
4.Hands-on Execises –Ingest Data from twitter and Analyze With
Hive
Machine Learning and Mahout
1.3Cs of Machine Learning
2.Introduction to Mahout
3.Hands-on Exercise:Build a Recommendation System using
Mahout
POCs
1.Banking Use case
2.Telecom Use case

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