Hadoop online training

Best Hadoop Online Training by Real time Experts

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Hadoop is an open source framework which is used for storing and processing the large scale of data sets on large

clusters of hardware. The specialty of Hadoop involves in HDFS which is used for storing data on large commodity

machines and provides very huge bandwidth for the cluster. Mainly, Hadoop uses Map Reduce Method for

processing large scale data sets. Lead Online Training provides the best online training for Hadoop by technical

experts in subject and will provide you the best training and makes you perfect in technology. We are always

available to support you.

Hadoop Online Training Course Overview: Basics of Hadoop:

Motivation for Hadoop

Large scale system training

Survey of data storage literature

Literature survey of data processing

Networking constraints

New approach requirements

Basic concepts of Hadoop:

What is Hadoop?

Distributed file system of Hadoop

Map reduction of Hadoop works

Hadoop cluster and its anatomy

Hadoop demons

Master demons

Name node

Tracking of job

Secondary node detection

Slave daemons

Tracking of task

HDFS(Hadoop Distributed File System)

Spilts and blocks

Input Spilts

HDFS spilts

Replication of data

Awareness of Hadoop racking

High availably of data

Block placement and cluster architecture

CASE STUDIES

Practices & Tuning of performances

Development of mass reduce programs

Local mode

Running without HDFS



Pseudo-distributed mode

All daemons running in a single mode

Fully distributed mode

Dedicated nodes and daemon running

Hadoop administration:

Setup of Hadoop cluster of Cloud era, Apache, Green plum, Horton works

On a single desktop, make a full cluster of a Hadoop setup.

Configure and Install Apache Hadoop on a multi node cluster.

In a distributed mode, configure and install Cloud era distribution.

In a fully distributed mode, configure and install Hortom works distribution

In a fully distributed mode, configure the Green Plum distribution.

Monitor the cluster

Get used to the management console of Horton works and Cloud era.

Name the node in a safe mode

Data backup.

Case studies

Monitoring of clusters

Hadoop Development :

Writing a MapReduce Program

Sample the mapreduce program.

API concepts and their basics

Driver code

Mapper

Reducer

Hadoop AVI streaming

Performing several Hadoop jobs

Configuring close methods

Sequencing of files

Record reading

Record writer

Reporter and its role

Counters

Output collection

Assessing HDFS

Tool runner

Use of distributed CACHE

Several MapReduce jobs (In Detailed)

1.MOST EFFECTIVE SEARCH USING MAPREDUCE

2.GENERATING THE RECOMMENDATIONS USING MAPREDUCE

3.PROCESSING THE LOG FILES USING MAPREDUCE



Identification of mapper

Identification of reducer

Exploring the problems using this application

Debugging the MapReduce Programs

MR unit testing

Logging

Debugging strategies

Advanced MapReduce Programming

Secondary sort

Output and input format customization

Mapreduce joins

Monitoring & debugging on a Production Cluster

Counters

Skipping Bad Records

Running the local mode

MapReduce performance tuning

Reduction network traffic by combiner

Partitioners

Reducing of input data

Using Compression

Reusing the JVM

Running speculative execution

Performance Aspects

CASE STUDIES

CDH4 Enhancements :

1. Name Node – Availability

2. Name Node federation

3. Fencing

4. MapReduce – 2

HADOOP ANALYST

1.Concepts of Hive

2. Hive and its architecture

3. Install and configure hive on cluster

4. Type of tables in hive

5. Functions of Hive library

6. Buckets

7. Partitions

8. Joins

1. Inner joins



2. Outer Joins

9. Hive UDF

PIG:

1.Pig basics

2. Install and configure PIG

3. Functions of PIG Library

4. Pig Vs Hive

5. Writing of sample Pig Latin scripts

6. Modes of running

1. Grunt shell

2. Java program

7. PIG UDFs

8. Macros of Pig

9. Debugging the PIG

IMPALA:

1. Difference between Pig and Impala Hive

2. Does Impala give good performance?

3. Exclusive features

4. Impala and its Challenges

5. Use cases

NOSQL:

1. HBase

2. HBase concepts

3. HBase architecture

4. Basics of HBase

5. Server architecture

6. File storage architecture

7. Column access

8. Scans

9. HBase cases

10. Installation and configuration of HBase on a multi node

11. Create database, Develop and run sample applications

12. Access data stored in HBase using clients like Python, Java and Pearl

13. Map Reduce client

14. HBase and Hive Integration

15. HBase administration tasks

16. Defining Schema and its basic operations.

17. Cassandra Basics



18. MongoDB Basics

Ecosystem Components:

1. Sqoop

2. Configure and Install Sqoop

3. Connecting RDBMS

4. Installation of Mysql

5. Importing the data from Oracle/Mysql to hive

6. Exporting the data to Oracle/Mysql

7. Internal mechanism

Oozie:

1. Oozie and its architecture

2. XML file

3. Install and configuring Apache

4. Specifying the Work flow

5. Action nodes

6. Control nodes

7. Job coordinator

Avro, Scribe, Flume, Chukwa, Thrift

1. Concepts of Flume and Chukwa

2. Use cases of Scribe, Thrift and Avro

3. Installation and configuration of flume

4. Creation of a sample application

Education

Hadoop online training