• 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
        • Traditional
        • NO SQL
  • Hadoop
    • What is Hadoop
    • History of Hadoop
    • Why Hadoop
    • Why Hadoop
      • Advantages and Disadvantages of Hadoop
        Importance of Different Ecosystems of Hadoop
        Importance of Integration with other BigData solutions
        Big Data Real time Use Cases

  • 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 HDFS>
    • Name Node failures
    • Secondary Name Node failures
    • Data Node failures
    • Where does it fit and Where doesn't fit
      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 features
    • Introduction to Namenode fedoration
      Introduction to Namenode High Availabilty
      Difference between Hadoop 1.x.x and Hadoop 2.x.x versions

  • Map Reduce architecture
    • Jobtracker
      • Importance of JobTracker
      • What are the roles of TaskTracker
      • What are the drawbacks in TaskTracker
    • TaskTracker
      • Importance of TaskTracker
      • What are the roles of TaskTracker
      • What are the drawbacks in TaskTracker
      • Map Reduce Job execution flow
  • 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 Reduce
    • How 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.
  • 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
    • What is difference between YARN and Map Reduce
  • Data Locality
    • What is Data Locality
    • Will Hadoop follows Data Localit
  • 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
  • Configurations
    • Can we change the existing configurations of mapreduce or not
    • Importance of configurations
    • Writing Unit Tests for Map Reduce Jobs
      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 Apache MapReduce Web UI

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
    • Script
    • 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 scripts
      How to integrate the Pig and Hbase

  • Hive Introduction
  • Hive architecture
    • Driver
    • Compiler
    • Semantic Analyzer
    • Hive Integration with Hadoop
      Hive Query Language(Hive QL)
      SQL VS Hive QL
      Hive Installation and Configuration
      Hive, Map-Reduce and Local-Mode
      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's
    • How to use the UDTF's in Hive
    • Importance of UDTF's in Hive
    • Transformations in Pig
      How to write a complex Hive queries
      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
  • SerDe
    • Importance of Hive SerDe's in production environment
    • How to write SerDe programs
    • How to integrate the Hive and Hbase

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

    Hbase introduction
    Hbase usecases
    Hbase basics
    • Column families
    • Scans
  • Hbase installation
    • Local mode
    • Psuedo mode
    • Cluster mode
  • Hbase Architecture
    • Storage
    • WriteAhead Log
    • Log Structured MergeTrees
  • Mapreduce integration
    • Mapreduce over Hbase
  • Hbase Usage
    • Key design
    • Bloom Filters
    • Versioning
    • Coprocessors
    • Filters
  • Hbase Clients
    • REST
    • Thrift
    • Hive
    • Web Based UI
  • Hbase Admin
    • Schema definition
    • Basic CRUD operations

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

Introduction to flume
Flume installation
Flume agent usage and Flume examples execution

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

Introduction to mahout
Mahout installation
Mahout examples

Introduction to Cassandra
Cassandra examples

Introduction to Storm
Storm examples

Introduction to MongoDB
MongoDB installation
MongoDB examples

Introduction to Nutch
Nutch Installation
Nutch Examples

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

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

Introduction to Amazon EMR and Amazon Ec2
How to use Amazon EMR and Amazon Ec2
Why to use Amazon EMR and Importance of this

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

  • 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)

  • Hadoop Installations
    • Local mode (hands on installation on ur laptop)
    • Psuedo mode (hands on installation on ur laptop)
    • Cluster mode (hands on 20 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)
  • Hive Installations
    • Local mode (hands on installation on ur laptop)
      • With internal Derby
    • Cluster mode (hands on installation on ur laptop)
      • With external Derby
      • With external MySql
    • Hive Web Interface (HWI) mode (hands on installation on ur laptop)
    • Hive Thrift Server mode (hands on installation on ur laptop)
    • 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
    • Local mode (hands on installation on ur laptop)
    • Psuedo mode (hands on installation on ur laptop)
    • Cluster mode (hands on installation on ur laptop)
      • With internal Zookeeper
      • With external Zookeeper
  • 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)
  • Flume Installation
    • Psuedo mode (hands on installation on ur laptop)
  • Oozie Installation
    • Psuedo mode (hands on installation on ur laptop)
  • Mahout Installation
    • Local mode (hands on installation on ur laptop)
    • Psuedo mode (hands on installation on ur laptop)
  • MongoDB Installation
    • Psuedo mode (hands on installation on ur laptop)
  • Nutch Installation
    • Psuedo mode (hands on installation on ur laptop)
  • Cloudera Hadoop Distribution installation
    • Hadoop
    • Hive
    • Pig
    • Hbase
    • Hue
  • HortonWorks Hadoop Distribution installation
    • Hadoop
    • Hive
    • Pig
    • Hbase
    • Hue

  • Hadoop and Hive Integration
  • Hadoop and Pig Integration
  • Hadoop and HBase Integration
  • Hadoop and Sqoop Integration
  • Hadoop and Oozie Integration
  • Hadoop and Flume Integration
  • Hive and Pig Integration
  • Hive and HBase integration
  • Pig and HBase integration
  • Sqoop and RDBMS Integration
  • Mahout and Hadoop Integration

  • Hands on MapReduce programming around 20+ programs these will make you to pefect in MapReduce both concept wise and programatically
  • Hands on 5 POC's will be provided (These POC's will help you perfect in Hadoop and it's ecosystems)
  • Hands on 20 Node cluster setup in our Lab.
  • Hands on installation for all the Hadoop and ecosystems in your laptop.
  • 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 BigData technology.
  • Real time projects explanation will be provided.
  • Mock Interviews will be conducted on one-to-one basis.
  • Discussing about hadoop interview questions daily base.
  • Resume preparation with POC's or Project's based on your experiance.