Enroll In ORIEN IT For Big Data Hadoop Free Demo On Every Sunday at 10 AM

      Our Hadoop training helps the students to get placed immediately after the completion of Big Data Hadoop Training. Our practical, real time Hadoop project scenarios and Hadoop Training help the candidate to work on Hadoop real time projects.

Overview of Hadoop Demo:

Everyone is free to attend this demo on Big Data Hadoop and gain some knowledge regarding the beneficial aspects one can attain by opting for our ORIEN IT institute offering job oriented Big Data Hadoop Training in Hyderabad with the best training faculty. This demo is attended by the experts of the industry having an immense knowledge on Hadoop practices. One can easily acquire a basic overview of all the functioning aspects of Hadoop by attending this free demo. 

Program Details 

• Course Duration – 55 Days
• Trainer: Kalyan
• Trainer Profile: Big Data Hadoop Trainer
• Mode Of Training: Both Online/Classroom
• Contact Person: Gopi
• Contact: 9703202345

website: http://www.orienit.com

Blogs: http://www.kalyanhadooptraining.com/

          http://kalyanbigdatatraining.blogspot.com/

===========Hadoop Course Content============

1.Introduction to Big Data and Hadoop

• Big Data

What is Big Data?

Why all industries are talking about Big Data?

What are the issues in Big Data?

• Storage
  • What are the challenges for storing big data?
• Processing
  • What are the challenges for processing big data?
• What are the technologies support big data?
  • Hadoop
  • Data Bases

1. Traditional
2. NO SQL

• HadoopImportance of Different Ecosystems of Hadoop
  • What is Hadoop?
  • History of HadoopWhy Hadoop?
  • Hadoop Use cases
  • Advantages and Disadvantages of Hadoop
• Importance of Integration with other Big Data solutions
• Big Data Real time Use Cases

2.HDFS (Hadoop Distributed File System)

• HDFS architecture
• Name Node
  • Importance of Name Node
  • What are the roles of Name Node
  • What are the drawbacks in Name Node

• Secondary Name Node
  • Importance of Secondary Name Node
  • What are the roles of Secondary Name Node
  • What are the drawbacks in Secondary Name Node
• Data Node
  • Importance of Data Node
  • What are the roles of Data Node
  • What are the drawbacks in Data Node
• Data Storage in HDFS
  • How blocks are storing in DataNodes
  • How replication works in Data Nodes
  • How to write the files in HDFS
  • How to read the files in HDFS
• HDFS Block size
  • Importance of HDFS Block size
  • Why Block size is so large?
  • How it is related to MapReduce split size
• HDFS Replication factor
  • Importance of HDFS Replication factor in production environment
  • Can we change the replication for a particular file or folder
  • Can we change the replication for all files or folders
• Accessing HDFS
  • CLI(Command Line Interface) using hdfs commands
  • Java Based Approach
• HDFS Commands
  • Importance of each command
  • How to execute the command
  • Hdfs admin related commands explanation
• Configurations
  • Can we change the existing configurations of hdfs or not?
  • Importance of configurations
• How to overcome the Drawbacks in HDFSWhere does it fit and Where doesn’t fit?
  • Name Node failuresSecondary
  • Name Node failures
  • Data Node failures
• Exploring the Apache HDFS Web UI
• How to configure the Hadoop Cluster
  • How to add the new nodes ( Commissioning )
  • How to remove the existing nodes ( De-Commissioning )
  • How to verify the Dead Nodes
  • How to start the Dead Nodes
• Hadoop 2.x.x version featuresDifference between Hadoop 1.x.x and Hadoop 2.x.x versions
  • Introduction to Namenode federation
  • Introduction to Namenode High Availabilty with NFS
  • Introduction to Namenode High Availabilty with QJM

3.MAPREDUCE

• Map Reduce architectureImportance of JobTracker
  • JobTracker
• What are the roles of JobTracker
• What are the drawbacks in JobTracker
• TaskTrackerMap Reduce Job execution flow
  • Importance of TaskTracker
  • What are the roles of TaskTracker
  • What are the drawbacks in TaskTracker
• Data Types in Hadoop
  • What are the Data types in Map Reduce
  • Why these are importance in Map Reduce
  • Can we write custom Data Types in MapReduce
• Input Format's in Map Reduce
  • Text Input Format
  • Key Value Text Input Format
  • Sequence File Input Format
  • NLine Input Format
  • Importance of Input Format in Map Reduce
  • How to use Input Format in Map ReduceHow to write custom Input Format's and its Record Readers
• Output Format's in Map Reduce
  • Text Output Format
  • Sequence File Output Format
  • Importance of Output Format in Map Reduce
  • How to use Output Format in Map Reduce
  • How to write custom Output Format's and its Record Writers
• Mapper
  • What is mapper in Map Reduce Job
  • Why we need mapper?
  • What are the Advantages and Disadvantages of mapper
  • Writing mapper programs
• Reducer
  • What is reducer in Map Reduce Job
  • Why we need reducer ?
  • What are the Advantages and Disadvantages of reducer
  • Writing reducer programs
• Combiner
  • What is combiner in Map Reduce Job
  • Why we need combiner?
  • What are the Advantages and Disadvantages of Combiner
  • Writing Combiner programs
• Partitioner
  • What is Partitioner in Map Reduce Job
  • Why we need Partitioner?
  • What are the Advantages and Disadvantages of Partitioner
  • Writing Partitioner programs
• Distributed Cache
  • What is Distributed Cache in Map Reduce Job
  • Importance of Distributed Cache in Map Reduce job
  • What are the Advantages and Disadvantages of Distributed Cache
  • Writing Distributed Cache programs
• Counters
  • What is Counter in Map Reduce Job
  • Why we need Counters in production environment?
  • How to Write Counters in Map Reduce programs
• Importance of Writable and Writable Comparable Api’s
  • How to write custom Map Reduce Keys using Writable
  • How to write custom Map Reduce Values using Writable Comparable
• Joins
  • Map Side Join

What is the importance of Map Side Join

Where we are using it

• Reduce Side Join

What is the importance of Reduce Side Join

Where we are using it

• What is the difference between Map Side join and Reduce Side Join?
• Compression techniques
  • Importance of Compression techniques in production environment
  • Compression Types

NONE, RECORD and BLOCK

• Compression Codecs

Default, Gzip, Bzip, Snappy and LZO

• Enabling and Disabling these techniques for all the Jobs
• Enabling and Disabling these techniques for a particular Job
• Map Reduce Schedulers
• FIFO Scheduler
• Capacity Scheduler
• Fair Scheduler
• Importance of Schedulers in production environment
• How to use Schedulers in production environment
• Map Reduce Programming Model
  • How to write the Map Reduce jobs in Java
  • Running the Map Reduce jobs in local mode
  • Running the Map Reduce jobs in pseudo mode
  • Running the Map Reduce jobs in cluster mode
• Debugging Map Reduce Jobs
  • How to debug Map Reduce Jobs in Local Mode.
  • How to debug Map Reduce Jobs in Remote Mode.
• Data Locality
  • What is Data Locality?
  • Will Hadoop follows Data Locality?
• Speculative Execution
  • What is Speculative Execution?
  • Will Hadoop follows Speculative Execution?
• Map Reduce Commands
  • Importance of each command
  • How to execute the command
  • Mapreduce admin related commands explanation
• ConfigurationsWriting Unit Tests for Map Reduce Jobs
  • Can we change the existing configurations of mapreduce or not?
  • Importance of configurations
• Configuring hadoop development environment using Eclipse
• Use of Secondary Sorting and how to solve using MapReduce
• How to Identify Performance Bottlenecks in MR jobs and tuning MR jobs.
• Map Reduce Streaming and Pipes with examples
• Exploring the MapReduce Web UI

4. YARN (Next Generation Map Reduce)

• What is YARN?
• What is the importance of YARN?
• Where we can use the concept of YARN in Real Time & it's powered projects
• What is difference between YARN and Map Reduce
• Yarn Architecture

1. Importance of Resource Manager
2. Importance of Node Manager
3. Importance of Application Manager
4. Yarn Application execution flow

• Installing YARN on both windows & Linux
• Exploring the YARN Web UI
• Examples on YARN

5. Apache PIG

• Introduction to Apache Pig
• Map Reduce Vs Apache Pig
• SQL Vs Apache Pig
• Different data types in Pig
• Modes Of Execution in Pig
• Local Mode
• Map Reduce Mode
• Execution Mechanism
• Grunt Shell
• Embedded
• UDF's
• How to write the UDF's in Pig
• How to use the UDF's in Pig
• Importance of UDF's in Pig
• Filter's
• How to write the Filter's in Pig
• How to use the Filter's in Pig
• Importance of Filter's in Pig
• Load Functions
• How to write the Load Functions in Pig
• How to use the Load Functions in Pig
• Importance of Load Functions in Pig
• Store Functions
• How to use the Store Functions in Pig
• Importance of Store Functions in Pig
• Transformations in Pig
• How to write the complex pig s
• How to integrate the Pig and Hbase

6.Apache HIVE

• Hive Introduction
• Hive architectureHive Query Language(Hive QL)
  • Driver
  • Compiler
  • Optimizer
  • Semantic Analyzer
• SQL VS Hive QL
• Hive Installation and Configuration
• Hive DLL and DML Operations
• Hive Services
  • CLI
  • Hiveserver
  • Hwi
• Metastore
  • embedded metastore configuration
  • external metastore configuration
• UDF's
  • How to write the UDF's in Hive
  • How to use the UDF's in Hive
  • Importance of UDF's in Hive
• UDAF's
  • How to use the UDAF's in Hive
  • Importance of UDAF's in Hive
• UDTF'sHow to write a complex Hive queries
  • How to use the UDTF's in Hive
  • Importance of UDTF's in Hive
• What is Hive Data Model?
• Partitions
  • Importance of Hive Partitions in production environment
  • Limitations of Hive Partitions
  • How to write Partitions
• Buckets
  • Importance of Hive Buckets in production environment
  • How to write Buckets
• SerDeHow to integrate the Hive and Hbase
  • Importance of Hive SerDe's in production environment
  • How to write SerDe programs

7.Cloudera Impala

• Introduction to Impala
• Impala Examples

8.Apache Zookeeper

• Introduction to zookeeper
• Pseudo mode installations
• Zookeeper cluster installations
• Basic commands execution

9.Apache HBase

• HBase introduction
• HBase usecases
• HBase basics
• Importane of Column families
• Basic CRUD operations

1. create
2. scan / getput
3. delete / drop

• Bulk loading in Hbase
• HBase installation
• Local mode
• Psuedo mode
• Cluster mode
• HBase Architecture
• HMaster
• HRegionServer
• Zookeeper
• Mapreduce integration
• Mapreduce over HBase

10.Apache Phoenix

• Introduction to Phoenix
• Installing Phoenix
• Integrating with Hbase
• Comparing Hbase & Phoenix
• Practice on Phoenix examples

11.Apache Cassandra

• Introduction to Cassandra
• Installing Cassandra
• Practice on Cassandra examples

12.MongoDB

• Introduction to MongoDB
• Installing MongoDB
• Practice on MongoDB examples

13.Apache Drill

• Introduction to Drill
• Installing Drill
• Practice on Drill examples

14.Apache SQOOP

• Introduction to Sqoop
• MySQL client and Server Installation
• Sqoop Installation
• How to connect to Relational Database using Sqoop
• Examples on Import and Export Sqoop commands

15.Apache FLUME

• Introduction to flume
• Flume installation
• Flume ArchitecturePractice on Flume examples
  • Agent
  • Sources
  • Channels
  • Sinks

16.Apache Kafka

• Introduction to Kafka
• Installing Kafka
• Practice on Kafka examples

17.Apache Spark

• Introduction to Spark
• Installing Spark
• Spark Architecture
• Introduction to Spark ComponentsPractice on Spark examplesSpark and Hive interation
  • Spark Core
  • Spark SQL
  • Spark Streaming
  • Spark MLLib
  • Spark GraphX

18.Apache OOZIE

• Introduction to oozie
• Oozie installation
• Executing different oozie workflow jobs
• Monitering Oozie workflow jobs

19.Real Time Big Data Projects

• We willl be sharing End-to-End Big Data Projects
• We are providing Big Data Project Practice on Our Lab
• We are providing Important Recorded Videos on Our YouTube Channel
• Any information search in Google / YouTube by keyword is 'Kalyan Hadoop'

20.Pre-Requisites for this Course

• Java Basics like OOPS Concepts, Interfaces, Classes and Abstract Classes etc (Free Java classes as part of course)
• SQL Basic Knowledge ( Free SQL classes as part of course)
• Linux Basic Commands (Provided in our blog)

Administration topics:

• Hadoop Installations (Windows & Linux)Hive Installations
  • Local mode (hands on installation on ur laptop)
  • Pseudo mode (hands on installation on ur laptop)
  • Cluster mode (hands on 40+ node cluster setup in our lab)
  • Nodes Commissioning and De-commissioning in Hadoop Cluster
  • Jobs Monitoring in Hadoop Cluster
  • Fair Scheduler (hands on installation on ur laptop)
  • Capacity Scheduler (hands on installation on ur laptop)
• Local mode (hands on installation on ur laptop)
  • With internal Derby
• Cluster mode (hands on installation on ur laptop)Hive Web Interface (HWI) mode (hands on installation on ur laptop)Hive Thrift Server mode (hands on installation on ur laptop)
  • With external Derby
  • With external MySql
• Derby Installation (hands on installation on ur laptop)MySql Installation (hands on installation on ur laptop)
• Pig Installations
  • Local mode (hands on installation on ur laptop)
  • Mapreduce mode (hands on installation on ur laptop)
• Hbase Installations
  • With internal Zookeeper
  • With external Zookeeper
  • Local mode (hands on installation on ur laptop)
  • Psuedo mode (hands on installation on ur laptop)
  • Cluster mode (hands on installation on ur laptop)
• Zookeeper Installations
  • Local mode (hands on installation on ur laptop)
  • Cluster mode (hands on installation on ur laptop)
• Sqoop Installations
  • Sqoop installation with MySql (hands on installation on ur laptop)
  • Sqoop with hadoop integration (hands on installation on ur laptop)
  • Sqoop with hive integration (hands on installation on ur laptop)
  • Sqoop with hbase integration (hands on installation on ur laptop)
• Flume Installation
  • Psuedo mode (hands on installation on ur laptop)
• Oozie Installation
  • Psuedo mode (hands on installation on ur laptop)
• Advanced Technologies InstallationsCloudera Hadoop Distribution installationHortonWorks Hadoop Distribution installation
  • Spark
  • Cassandra
  • MongoDB
  • Kakfa
  • Mahout

21.ORIENIT Hadoop POC's Solution Class

22.================================

23.Advanced and New technologies architectural discussions

• Spark / Flink (Real time data processing)
• Storm / Kafka / Flume (Real time data streaming)
• Cassandra / MongoDB (NOSQL database)
• Solr (Search engine)
• Nutch (Web Crawler)
• Lucene (Indexing data)
• Mahout (Machine Learning Algorithms)
• Ganglia, Nagios (Monitoring tools)
• Cloudera, Hortonworks, MapR, Amazon EMR (Distributions)
• How to crack the Cloudera / Hortonworks certification questions

Cloudera Distribution

• Introduction to Cloudera
• Cloudera Installation
• Cloudera Certification details
• How to use cloudera hadoop
• What are the main differences between Cloudera and Apache hadoop

Hortonworks Distribution

• Introduction to Hortonworks
• Hortonworks Installation
• Hortonworks Certification details
• How to use Hortonworks hadoop
• What are the main differences between Hortonworks and Apache hadoop

Amazon EMR

• Introduction to Amazon EMR and Amazon EC2
• How to use Amazon EMR and Amazon EC2
• Why to use Amazon EMR and Importance of this

Hadoop ecosystem Integrations:

• Hive and Spark integration
• Hive and HBase integration
• Pig and HBase integration
• Sqoop and RDBMS integration
• Hbase and Phoenix integration
• Flume and Phoenix integration
• Kakfa and Phoenix integraion

Free Big Data Workshops:

• Spark & Scala
• Cassandra
• MongoDB
• Search engine & E-commerce solutions
• Big Data Analytics (R, Mahout, Spark ML)

24.What we are offering to you:

• Hadoop installation on both Windows & Linux
• Free Weekly Online Hadoop Certification
• Real Time Big Data projects will be shared
• Free Big Data Workshops on new & advanced technologies
• Hands on MapReduce programming around 20+ programs these will make you to perfect in MapReduce both concept-wise and programmatically
• Hands on 5 POC's will be provided (These POC's will help you perfect in Hadoop and it's ecosystems)
• Hands on practical 40+ Node hadoop cluster setup in our Lab.
• Well documented Hadoop material with all the topics covering in the course
• Well documented Hadoop blog contains frequent interview questions along with the answers and latest updates on Big Data technology.
• Discussing about hadoop interview questions & answers daily base.
• Resume preparation with POC's or Project's based on your experience.