Big Data Hadoop Certification Training Course in Jaipur

 

Course Name – Big Data Hadoop Course

Duration – 1.5 Months

 

 

Hadoop has the power to store also as process bulks of knowledge in any format. With data volumes going larger day by day with the evolution of social media, considering this technology is basically , really important.

 

Unmatched computing power: The distributed computing model of Hadoop processes big data during a fast pace. The more computing nodes, the more processing power.

 

Effective fault tolerance: there’s no got to panic in hardware failure as Hadoop has the power to guard data and applications. just in case a node fails, jobs are automatically redirected to other nodes hence no obstruction in distributed computing. It also stores multiple copies of knowledge.

 

Superb flexibility: there’s no got to preprocess data before its storage just you want to do in conventional relational databases. you’ll store as much data as you would like and use it later. Unstructured, text, images and videos also can be stored easily.

 

Scalability: By adding nodes you’ll enhance your system to handle more data. there’s no got to be a pro in system administration.

 

Affordable: because the open source network is free, it uses commodity hardware for the storage of huge data.

 

Big Data Hadoop Certification Training Course Highlights

 

1. Hands-on Training

2. Course Contents as suggested by HP

3. Instructor led Training

4. Relevant Project Work

5. HP Certificate

6. Training by Highly Experienced Professional

 

Take Away – Big Data Hadoop Certification Training Course

 

This course covers the basics of the powerful and versatile Hadoop platform and lays the arm foundation for the development of your Hadoop knowledge to know the meaning behind Big Data.

 

Target Audience – Big Data Hadoop Certification Training Course

 

Fresher’s with computer science knowledge, Administrators, System Engineers, Developers, and Project managers.

 

Prerequisites – Big Data Hadoop Certification Training Course

 

• Computer Fundamentals,

• Windows OS 

• Basics of programing language 

• Basics of Unix/Linux OS

• Core Java

• Basic SQL

 

Objectives : Big Data Hadoop Certification Training Course

 

• Big Data Usage

• Hadoop

• Live Experience

 

Recommended Next Course

 

• Certification Exams

 

Hadoop Administrator Course

 

 

      1.The Motivation & Limitation for Hadoop

 

• Problems with TraditionalLarge-Scale Systems

• Why Hadoop Hadoop Fundamental Concepts

• History of Hadoop with Hadoopable problems

• Motivation & Limitation of Hadoop

• Available version Hadoop 1.x & 2.x

• Available Distributions of Hadoop (Cloudera, Hortonworks)

• Hadoop Projects & Components

• The Hadoop Distributed filing system (HDFS)

 

      2.Hadoop Ecosystem Cluster

 

Hadoop Ecosystem projects & Components overview 

 

•  HDFS – file system

•  HBase – The Hadoop Database

•  Cassandra – No-SQL Database

•  Hive – SQL Engine

•  Mahout

 Hadoop Architecture overview Cluster Daemons Its Functions

 

 

•  Name Node

•  Secondary Node

•  Data Nodes

 

3. Planning Hadoop Cluster Initial Configuration

 

• General Planning Considerations

• Choosing the proper Hardware

• Network Considerations

• Configuring Nodes

• Planning for Cluster & Its Management

• Types of Deployment

• Cloudera Manager

 

4. Installation & Deployment of Hadoop

 

• Installing Hadoop (Cloudera)

• Installation – Pig, Hive, HBase, Cassandra etc

• Specifying the Hadoop Configuration

• Performing Initial HDFS Configuration

• Performing Initial YARN and MapReduce Configuration

• Hadoop Logging Cluster Monitoring

 

5. Load Data and Run Application

 

• Ingesting Data from External Sources withFlume

• Ingesting Data from Relational Databaseswith Sqoop

• REST Interfaces

• Best Practices for Importing Data

 

6. Manage, Maintain, Monitor, and troubleshoot of cluster

 

• General System Monitoring

• Monitoring Hadoop Clusters

• Common Troubleshooting Hadoop Clusters

• Common Misconfigurations

• Managing Running Jobs

• Scheduling Hadoop Jobs

 

• Upgrade, Rolling andBackup

 

• Cluster Upgrading

• Checking HDFS Status

• Adding and Removing Cluster Nodes

• Name Node Metadata Backup

• Data Backup

• Distributed Copy

• Parallel Data Ingestion

 

• Conclusion & FAQs

 

Hadoop Developer Course

 

 

• Introduction to Hadoop and big Data

 

• Introduction to Big Data

• Introduction to Hadoop

• Why Hadoop & Hadoop Fundamental Concepts

• History of Hadoop with Hadoopable problems

• Scenarios where Hadoop is used

• Available version Hadoop 1.x & 2.x

• Overview of batch processing and real time data analytics using Hadoop

• Hadoop vendors – Apache , Cloudera , Hortonworks

• Hadoop services – HDFS , MapReduce , YARN

• Introduction to Hadoop Ecosystem components ( Hive, Hbase, Pig, Sqoop, Flume, Zookeeper, Oozie, Kafka, Spark )

 

• Cluster setup ( Hadoop 1.x )

 

• Linux VM installation on system for Hadoop cluster using Oracle Virtual Box

• Preparing nodes for Hadoop and VM settings

• Install Java and configure password less SSH across nodes

• Basic Linux commands

• Hadoop 1.x single node deployment

• Hadoop Daemons – NameNode, JobTacker, DataNode, TaskTracker, Secondary NameNode

• Hadoop configuration files and running

• Important web URLs and Logs for Hadoop

• Run HDFS and Linux commands

• Hadoop 1.x multi-mode deployment

• Run sample jobs in Hadoop single and multi-node clusters

 

• HDFS Concepts

 

• HDFS Design Goals

• Understand Blocks and the way to configure block size

• Block replication and replication factor

• Understand Hadoop Rack Awareness and configure racks in Hadoop

• File read and write anatomy in HDFS

• Enable HDFS Tash

• Configure HDFS Name and space Quota

• Configure and use WebHDFS ( Rest API For HDFS )

• Health monitoring using FSCK command

• Understand NameNode Safemode, file system image and edits

• Configure Secondary NameNode and use checkpointing process to provide NameNode failover

• HDFS DFSAdmin and file system shell commands

• Hadoop NameNode / DataNode directory structure

• HDFS permissions model

• HDFS Offline Image Viewer

 

• MapReduce Concepts

 

• Introduction to MapReduce

• MapReduce Architecture

• Understanding the concept of Mappers & Reducers

• Anatomy of MapReduce program

• Phases of a MapReduce program

• Data-types in Hadoop MapReduce

• Driver, Mapper and Reducer classes

• InputSplit and RecordReader

• Input format and Output format in Hadoop

• Concepts of Combiner and Partitioner

• Running and Monitoring MapReduce jobs

• Writing your own MapReduce job using MapReduce API

 

• Cluster setup ( Hadoop 2.x )

 

• Hadoop 1.x Limitations

• Design Goals for Hadoop 2.x

• Introduction to Hadoop 2.x

• Introduction to YARN

• Components of YARN – Resource Manager, Node Manager, Application Master

• Deprecated properties

• Hadoop 2.x Single node deployment

• Hadoop 2.x Multi node deployment

 

• HDFS High Availability and Federation

 

• Introduction to HDFS Federation

• Understand Name service ID and Block pools

• Introduction to HDFS High Availability

• Failover mechanisms in Hadoop 1.x

• Concept of Active and StandBy NameNode

• Configuring Journal Nodes and avoiding split brain scenario

• Automatic and manual failover techniques in HA using Zookeeper and ZKFC

• HDFS HAadmin commands

 

• YARN – yet another Resource Negotiator

 

• YARN Architecture

• Yarn Components – Resource Manager, Node Manager, Job History Server, Application Timeline Server, MR Application Master

• YARN Application execution flow

• Running and Monitoring YARN Applications

• Understand and Configure Capacity / Fair Schedulers in YARN

• Define and configure Queues

• Job History Server / Application Timeline Server

• YARN Rest API

• Writing and executing YARN applications

 

• Hive

 

• Problems with No-SQL Database

• Introduction & Installation Hive

• Data Types & Introduction to SQL

• Hive-SQL: DML & DDL

• Hive-SQL: Views & Indexes

• Hive User Defined Functions

• Configuration to HBase

• Hive Thrift Service

• Introduction to HCatalog

• Install and configure HCatalog services

 

• Apache Flume 

 

• Introduction to Flume

• Flume Architecture and Installation

• Define Flume agents – Sink, Source and Channel

• Flume Use cases

 

• Apache Pig

 

• Introduction to Pig

• Pig Installation

• Accessing Pig Grunt Shell

• Pig data Types

• Pig Commands

• Pig Relational Operators

• Pig User Defined Functions

• Configure PIG to use HCatalog

 

• Apache Sqoop

 

• Introduction to Sqoop

• Sqoop Architecture and installation

• Import Data using Sqoop in HDFS

• Import all tables in Sqoop

• Export data from HDFS

 

• Apache Zookeeper

 

• Introduction to Apache Zookeeper

• Zookeeper stand alone installation

• Zookeeper Clustered installation

• Understand Znodes and Ephemeral nodes

• Manage Znodes using Java API

• Zookeeper four letter word commands

 

• Apache Oozie

 

• Introduction to Oozie

• Oozie Architecture

• Oozie server installation and configuration

• Design Workflows, Coordinator Jobs, Bundle Jobs in Oozie

 

• Apache Hbase

 

• Introduction to Hbase

• Hbase Architecture

• HBase components – Hbase master and Region servers

• Hbase installation and configurations

• Create sample tables and queries on HBase

 

• Apache Spark / Storm / Kafka

 

• Real Time data Analytics

• Introduction to Spark / Storm / Kafka

 

• Cluster Monitoring and Management tools

 

• Cloudera Manager

• Apache Ambari

• Ganglia

• JMX monitoring and Jconsole

• Hadoop User Experience ( HUE )