Quest Learning

by IIM Lucknow Alumna

Big Data Hadoop is an open-source software framework for storage and large-scale processing of data-sets on clusters of commodity hardware. we are offers hadoop training in Hyderabad by certified professionals. Big data Hadoop is an Apache top-level project being built and used by a global community of contributors and users.It is licensed under the Apache License 2.0. The Apache Hadoop framework is composed of the following modules: Hadoop Common – contains libraries and utilities needed by other Hadoop modules. Big data Hadoop Distributed File System (HDFS) – a distributed file-system that stores data on commodity machines, providing very high aggregate bandwidth across the cluster. Hadoop YARN – a resource-management platform responsible for managing compute resources in clusters and using them for scheduling of users’ applications. Hadoop MapReduce – a programming model for large scale data processing .


Through instructor-led lessons and interactive, hands-on exercises, participants will navigate the Hadoop ecosystem, learning topics such as: How big data Hadoop fits into the world (recognize the problems it solves) Understanding the concepts of HDFS and MapReduce (find out how it solves the problems) Writing MapReduce programs (see how we solve the problems) Solving problems on your own.

  • Motivation for Big data Hadoop
    1. Problems with traditional large-scale systems
    2. Requirements for a new approach
  • Big data Hadoop Basic Concepts
  • Writing a Map Reduce Program
  • Integrating Hadoop into the Workflow
  • Delving Deeper Into The Hadoop API
  • Using Hive and Pig
  • Common Map Reduce Algorithms
  • Practical Development Tips and Techniques
  • More Advanced MapReduce Programming
  • Joining Data Sets in MapReduce Jobs
  • Graph Manipulation in big data Hadoop
  • Creating Workflows with Oozie


  • Big Data
  • What is Big Data Concept?
  • Why Industries are using Big Data
  • What are the issues with Big Data
  • Big Data Storage
  • What are the challenges for storing big data
  • Processing
  • What are the Big Data challenges and Opportunities?
  • What are the Big Data technologies & techniques?
  • Hadoop Big Data
  • Databases
  • Traditional Database
  • Hadoop
  • What is Hadoop Technology?
  • Brief History of Hadoop
  • Why Hadoop is needed?

Advantages and Disadvantages of Hadoop technology

Importance of Different Ecosystem of Hadoop

Importance of Integration with other Big Data solutions

Real time Big Data Analytic Use Cases

Introduction to Bigdata and Hadoop

  • HDFS Architecture Basics
  • Namenode
  • Importance of Name Node
  • What is the function of Name Node
  • Name Node Drawbacks
  • Secondary Name Node
  • Secondary Name Node Importance
  • What is the function of Secondary Name Node
  • Secondary Name Node Drawbacks
  • Data Node
  • Importance of Data Node
  • What are the roles of Data Node
  • Drawbacks of Data Node
  • How is Data stored in HDFS
  • How blocks are storing in Data Nodes
  • How Node replication works in Data Nodes
  • How to write files to HDFS
  • How to read files to HDFS
  • HDFS Block size sttings
  • HDFS Block size configuration
  • Why is a Block in HDFS so large
  • How it is related to Mapreduce input split size
  • HDFS Replication factor settings
  • HDFS Replication factor Importance in production environment
  • Replication for all files or folders
  • Accessing HDFS
  • using HDFS CLI(Command Line Interface) commands
  • Java Based Approach
  • HDFS Commands List
  • Importance command
  • How to execute the hadoop command
  • Can we change the existing configurations of hdfs or not
  • Importance of Hadoop configurations
  • How to overcome the Disadvantages of HDFS
  • Hdfs admin related commands explanation


  • Name node failure Hadoop
  • Secondary Name Node failures
  • Hadoop Data Node failures

Where does it run and Where doesn’t run

Exploring the Apache HDFS Hadoop Web UI

  • Hadoop cluster configuration
  • How to add the new nodes (Commissioning tool )
  • How to remove the existing nodes (De-Commissioning tool )
  • How to list Dead Nodes

Hadoop 2.x.x version features

Introduction to Hadoop Namenode fedoration

Hadoop Namenode High Availability

Difference between Hadoop 1.x and 2.x versions


  • Hadoop Map Reduce architecture
  • Job Tracker
  • Importance of Job Tracker in Hadoop
  • What is the function of job Tracker
  • What are the disadvantages of job Tracker
  • Task Tracker
  • Importance of Task Tracker in hadoop
  • What is the function of Task Tracker
  • What are the disadvantages of Task Tracker
  • How to run a Map Reduce Job execution flow
  • Hadoop Data Types
  • Data types in Map Reduce
  • Importance of Map Reduce
  • Custom Data Types in Mapreduce
  • Map Reduce Input Formats
  • Text Input Format in Hadoop
  • Key Value Text Input Format Example
  • Sequence File Input Format in Hadoop
  • N-line Input Format
  • Advantages of Input Format in Map Reduce
  • Where to use Input Format in Map Reduce
  • How to custom Input Format & its Record Readers
  • Output Formats in Map Reduce
  • Text Output Formats
  • Sequence File Output Formats
  • Advantages 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


  • What is mapper in Mapreduce Job
  • Use of mapper?
  • Mapper Advantages and Disadvantages
  • Writing mapper programs


  • Reducer in Map Reduce Job
  • What is the need of reducer
  • Advantages and Disadvantages of reducer
  • Writing reducer programs


  • What is combiner in Map Reduce Job
  • combiner need?
  • Advantages & Disadvantages of Combiner
  • Writing Combiner programs


  • What is Partitioner in Map Reduce Job
  • Partitioner need
  • What are the Advantages and Disadvantages of Partitioner
  • How to write Partitioner programs

Distributed Cache

  • Distributed Cache in Map Reduce Job
  • Distributed Cache Importance in Map Reduce job
  • Advantages and Disadvantages of Distributed Cache
  • Writing Distributed Cache programs


  • What is Counter in Map Reduce Job
  • Why we need counters in production environment
  • How to Write Counters in Map Reduce programs

Writable & Writable Comparable Api’s Importance

  • How to write custom Map Reduce Keys using Writable
  • How to write custom Map Reduce Values using Writable Comparable


  • Map Side Join
  • Map Side Join Importance
  • Where we are using it
  • Reduce Side Join
  • Reduce Side Join Importance
  • 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
  • Compression Codecs
  • Default, Gzip, Bzip, LZO and Snappy
  • Techniques for Enabling and Disabling all the Jobs
  • Techniques Enabling and Disabling for a particular Job
  • Mapreduce Schedulers
  • FIFO(First in First Out) Scheduler
  • Capacity Scheduler Examples
  • Hadoop Fair Scheduler
  • What is YARN Hadoop
  • What is the importance of YARN Hadoop
  • Where we can use the concept of YARN Hadoop in Real Time
  • What is difference between YARN & Mapreduce
  • Hadoop Data Locality
  • What is Hadoop Data Locality ?
  • Will Hadoop follows Data Locality
  • Speculative Execution in Hadoop
  • What is Speculative Execution in Hadoop?
  • Will Hadoop follows the Speculative Execution
  • Mapreduce Commands
  • Importance of each Mapreduce command
  • How to execute the Mapreduce command
  • Mapreduce admin related commands explanation Configurations
  • Is it possible to change the existing Mapreduce configuration or not Is it possible to
  • Importance of Mapreduce configurations

Writing Unit Tests for Mapreduce Jobs

How to Configure Hadoop development environment using Eclipse

Use of Secondary Sorting method and how to solve using MapReduce

How to identify Performance of Bottlenecks in Mapreduce jobs and tuning Mapreduce jobs.

Map Reduce Streaming & Pipes with examples

Exploring the Apache Hadoop Mapreduce Web UI


Introduction to Apache Hadoop Pig

Apache Pig vs Mapreduce

Apache pig vs SQL

Data types in Pig

  • Various Modes Of Execution in Pig
  • local mode in pig
  • mapreduce mode in pig
  • Execution based Mechanism
  • Grunt Shell Pig
  • Pig Script
  • Embedded
  • UDF’s in Pig
  • writing  UDF Pig
  • How to use the UDF’s Pig Hadoop
  • Importance of Pig UDF
  • Filter in Pig
  • Writing Filter’s in Pig
  • Use of Filter’s in Pig
  • Filter’s Importance in Pig

Load Functions in Pig

  • Writing Load Functions in Pig
  • Using Load Functions in Pig
  • Load Functions Importance in Pig

Store Functions

  • Using Store Functions in Pig
  • Store Functions Importance in Pig

Transformations in Pig

Writing complex pig scripts

Integrate the Pig and Hbase

Optimization Techniques

Filter Logic Expression Simplifier

Split Filter

Push Up Filter

Merge Filter

Push Down for each Flatten

Limit Optimizer

Column Map Key Prune

Add For each

Merge for each

Group by Const Parallel Setter

Performance Enhancers


Use various Types

Project Early and Often

Filter Early and Often

Reducing Operator Pipeline

Make Pig UDFs Algebraic

Use of Accumulator Interface

Drop Nulls

Advantage of Join Optimizations

Use of Parallel Features

Use of LIMIT Operator

Compressing pig Results of Intermediate Jobs

Combine Small Input Files format

Specialized Joins and Replicated Joins

Skewed Joins and Merge Joins

Performance into Considerations


  • Hive Introduction Hadoop
  • Hive architecture Explanation
  • Hive Driver Jar
  • Hive query Compiler
  • Semantic compiler Analyzer

Hive Integration with Big Data Hadoop

Hive Query Language

Sql vs Hiveql

Hive Installation & Configuration

Hive, Mapreduce & Local-Mode

Hive DLL & DML Operations

Hive Services Jar

  • CLI Hive
  • Hive Server
  • Hwi hive

Metastore in Hive

  • Hive embedded Meta store configuration
  • Hive External Meta store configuration


  • Writing UDF’s in Hive
  • UDF’s uses in Hive
  • UDF’s Importance in Hive


  • UDAF’s uses in hive
  • UDAF’s Importance in hive


  • UDTF’s uses in Hive
  • UDTF’s importance in Hive

Transformations in Hadoop Pig

Writing complex Hive queries

Hive Data Model


  • Hive Partitions importance in production environment
  • Hive Partition Limitations
  • Writing Partitions


  • Hive Buckets Importance in production environment
  • Writing Buckets


  • Bucktized Joins
  • SMB
  • Ventilzation
  • RC
  • ORC
  • Integration of Hive and H base


Basics of zookeeper

Installation of Pseudo mode

Installation of Zookeeper cluster

Execution of Basic commands


Introduction of Hbase

Use cases of Hbase

Basic Hbase

  • Local mode
  • Column families
  • Scans

Hbase installation

  • Cluster mode
  • Psuedo mode
  • Coprocessors

Hbase Architecture

  • Log Structured MergeTrees
  • Mapreduce over Hbase
  • Storage
  • Write a head Log


Hbase Usage

  • Filters
  • Bloom Filters
  • Versioning
  • Key design

Hbase Clients

  • Web Based UI

Hbase Admin

  • Basic CRUD operations
  • Schema definition
  • Basic CRUD operations


Introduction to Sqoop

Sqoop Commands and Examples on Import & Export commands

Sqoop Installation

How to connect to Relational Database using Sqoop

MySQL client and Server Installation


Flume Introduction

Installation of Flume

Flume agent usag with examples execution


Oozie Introduction

Installation of Oozie

Data scientist vs Data analyst

Execution of oozie workflow jobs

Monitering of Oozie workflow jobs


  • Batch versus real-time data processing
  • Exploring the Spark shell -> Creating Spark Context.
  • Introduction to Spark, Spark versus Hadoop
  • Coding Spark jobs in Scala
  • RDD Programming
  • Transformations
  • Broad cast variables
  • Key Value Pair RDD.
  • Architecture of Spark.
  • Operations on RDD.
  • Actions
  • Loading Data and Saving Data.


Cloudera Introduction

Installation of Cloudera

Certification details of Cloudera

cloudera hadoop uses

Difference between Cloudera and Apache hadoop


  • Java Basics like OOPS Concepts, Abstract , Classes and Interfaces
  • Free Java classes as part of Hadoop course
  • Basic SQL Knowledge
  • Basic Linux Commands


  • Installation of Hadoop
  • Local mode
  • Cluster mode
  • Psuedo mode
  • Jobs Monitoring in Hadoop Cluster
  • Capacity Scheduler
  • Nodes Commissioning and De-commissioning in Hadoop Cluster
  • Fair Scheduler

Installation of Hive

  • Cluster mode
  • Local mode
  • Hive Web Interface (HWI) mode
  • With external Derby
  • With external MySql
  • With internal Derby
  • Hive Thrift Server mode
  • Derby Installation
  • Installation of MySql

Pig Installations

  • Local mode
  • Mapreduce mode

Hbase Installations

  • Local mode
  • Cluster mode
  • Psuedo mode
  • Internal Zookeeper
  • External Zookeeper

Zookeeper Installations

  • Local mode
  • Cluster mode

Sqoop Installations

  • Installation of Sqoop with MySql
  • Sqoop with hive integration
  • Sqoop with hadoop integration

Oozie Installation

  • Psuedo mode

Flume Installation

  • Psuedo mode

Cloudera Hadoop Distribution installation

  • Hadoop
  • Hbase
  • Hue
  • Hive
  • Hive
  • Pig

HortonWorks Hadoop Distribution installation

  • Hue
  • Hbase
  • Hadoop
  • Pig


  • Hadoop Hive Integration
  • Hadoop & Oozie Integration
  • Hadoop and HBase Integration
  • Hadoop and Sqoop Integration
  • Pig and HBase integration
  • Spark and  Scala basics and Hadoop Integration
  • Hadoop and Pig Integration
  • Hive and Pig Integration
  • Sqoop and RDBMS Integration
  • Hadoop and Flume Integration
  • Hive and HBase integration

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