Big Data Hadoop

Indeed Inspiring’s Big Data Hadoop Training Course is curated by Hadoop industry experts, and it covers in-depth knowledge on Big Data and Hadoop Ecosystem tools such as HDFS, YARN, MapReduce, Hive, Pig, HBase, Spark, Oozie, Flume and Sqoop

  • Explain The Need For Big Data, And List Its Applications
  • Demonstrate The Mastery Of Hdfs Concepts And Mapreduce Framework
  • Design And Propose Solution For A Given Big Data Problem
  • Participate In Big Data Adoption And Planning Projects
  • Install And Configure Big Data Tools
  • Make Predictions Using Machine Learning
  • Understand The Overall Design Goals Of Each Of Hadoop Schedulers
  • Understand The Functions And Features Of Hadoop’S Metric Collection Abilities
  • Discuss And Differentiate Various Commercial Distributions Of Big Data Like Cloudera And Hortonworks
  • Differentiate Between Hadoop 1.0 And Hadoop 2.0
  • Weekend Batch Is Consisting Of 16 Classes Each Running For 5 Hours.
  • Weekday Batch Is Consisting Of 40 Classes Each Running For 2 Hours.
  • Total 80 Hours.
  • Pre- Training
  • Actual- Training
  • Post-Training


  • Introduction To Big Data Technology Stack
  • Introduction To Hadoop And Ecosystem Build
  • Understanding Cluster Setup Activities
  • Hdfs Architecture
  • Hive Architecture
  • Pig Architecture
  • Introduction To Nosql
  • Hbase Architecture
  • Understanding Cloudera Manager And Hue
Describe the function of HDFS daemons
  • Describe The Normal Operation Of An Apache Hadoop Cluster, Both In Data Storage And In Data Processing
  • Identify Current Features Of Computing Systems That Motivate A System Like Apache Hadoop
  • Classify Major Goals Of Hdfs Design
  • Given A Scenario, Identify Appropriate Use Case For Hdfs Federation
  • Identify Components And Daemon Of An Hdfs Ha-Quorum Cluster
  • Analyze The Role Of Hdfs Security (Kerberos)
  • Determine The Best Data Serialization Choice For A Given Scenario
  • Describe File Read And Write Paths
  • Identify The Commands To Manipulate Files In The Hadoop File System Shell
  • Understand How To Deploy Core Ecosystem Components, Including Spark, Impala, And Hive
  • Understand How To Deploy Mapreduce V2 (Mrv2 / Yarn), Including All Yarn Daemons
  • Understand Basic Design Strategy For Yarn And Hadoop
  • Determine How Yarn Handles Resource Allocations
  • Identify The Workflow Of Job Running On Yarn
  • Determine Which Files You Must Change And How
Hadoop Cluster Installation and Administration
  • Given A Scenario, Identify How The Cluster Will Handle Disk And Machine Failures
  • Analyze A Logging Configuration And Logging Configuration File Format
  • Understand The Basics Of Hadoop Metrics And Cluster Health Monitoring
  • Identify The Function And Purpose Of Available Tools For Cluster Monitoring
  • Be Able To Install All The Ecoystme Components In Cdh 5, Including (But Not Limited To): Impala, Flume, Oozie, Hue, Cloudera Manager, Sqoop, Hive, And Pig
  • Identify The Function And Purpose Of Available Tools For Managing The Apache Hadoop File System
Resource Management
  • Understand The Overall Design Goals Of Each Of Hadoop Schedulers
  • Given A Scenario, Determine How The Fifo Scheduler Allocates Cluster Resources
  • Given A Scenario, Determine How The Fair Scheduler Allocates Cluster Resources Under Yarn
  • Given A Scenario, Determine How The Capacity Scheduler Allocates Cluster Resources
Monitoring and Logging
  • Understand The Functions And Features Of Hadoop’S Metric Collection Abilities
  • Analyze The Namenode And Jobtracker Web Uis
  • Understand How To Monitor Cluster Daemons
  • Identify And Monitor Cpu Usage On Master Nodes
  • Describe How To Monitor Swap And Memory Allocation On All Nodes
  • Identify How To View And Manage Hadoop’S Log Files
  • Interpret A Log File
Hadoop Developer
  • Mapreduce

    • Introduction To Mapreduce
    • Mapreduce Engine
      – Jobtracker
      – Tasktracker
    • Mapreduce Programming Model
      – Mapper Class
      – Reduce Class
    • Executing Mapreduce Jobs
    • Mapreduce And Java
    • Mapreduce Programs In Java And Eclipse
Hive – The Data Warehouse in Hadoop
  • Concepts Of Hive

    • Hive Architecture
    • Metastore
    • Driver
    • Thrift Server
    • Web Interface
    • Jdbc / Odbc
    • CLI
    • Introduction To Hql (Hive Query Language)
    • The Hive Data Model
    • Partitions
    • Data Types
    • Hive Configuration
    • Sample Hive Queries And Commands
  • Pig Execution Mode
  • Local Mode
  • Mapreduce Mode
  • Pig Engine
  • Pig Latin Scripts
  • Interactive Mode
  • Batch Mode
  • Configuring Pig
  • Sample Pig Scripts
Hadoop Data Analytics
  • Working With Hive-E-Commerce Use Case
  • Working With Pig-Financial Uses Case
  • Twitter Use Case-Sentimental Analysis
  • Mr Optimization
  • Custom Combiner, Custom Partitioner And Distributed Cache
  • Advanced Mapreduce
  • Datatypes In Mapreduce
  • Input Formats In Mapreduce
  • Output Formats In Mapreduce
  • Joins In Mapreduce
  • Reduce Side Join
  • Replicated Join
  • Composite Join
  • Use Cases Of Mapreduce
Hadoop Data Ingestion
    • Sqoop, Flume And Kafka – Moving Data To And From Hdfs
    • Introduction To Sqoop
    • Sqoop Connectors To Rdbms
    • Importing Data From Sqoop To Hive
    • Sqoop Commands
    • Introduction To Flume
    • Flume Data Model
    • Flume Examples
    • Use Cases Of Sqoop And Flume
    • Introduction To Kafka
    • Basic Operations
    • Consumer Group Examples
NOSQL Databases
  • Introduction To Nosql Databases
  • History Of Nosql
  • Rdbms Vs Nosql Comparision
  • Popular Nosql Databases
Project Use Cases
  • Entertainment Use Case
  • Twitter Use Case
  • Health Care Use Case
  • E-Commerce Use Case
  • Bio-Informatics Use Case
  • Multi-Node Cluster Deployment
  • Hdfs Ha Configuration
  • Rm Ha Configuration
  • Implementing Custom Schedulers
Course Objectives
  • NOSQL Movement
  • Hbase Architecture
  • Region Servers
  • Hbase Storage
  • Introduction To Zookeeper
  • Entities Of Zookeeper
  • Leader
  • Follower
  • Observer
  • Zookeeper Data Model
  • Configuring Hbase And Zookeeper
  • Hbase Examples
  • Hbase Use Cases
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