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Big Data Hadoop

In today’s digital world, the amount of data generated daily has grown tremendously from social media sites, purchase transaction records, and cell phone GPS signals. This amount of large data needs analysis which provides precision services to everyone. Big Data is the need of the hour.

BIG DATA HADOOP ADMIN

  • Characteristics of Big Data
  • Big data collection and cleanup
  • Why analyze big data
  • Why parallel computing important
  • Various products for handling big data
  • Hadoop Stack
  • Components of Hadoop
  • Starting Hadoop
  • Various Hadoop processes
  • Hands On
  • Hadoop Stack
  • Components of Hadoop
  • Starting Hadoop
  • Various Hadoop processes
  • Hands On
  • Basic file commands
  • Reading & writing to files
  • Run a word count on a large text file
  • Web based UI
  • View jobs status on Hadoop prompt
  • View jobs status on web UI
  • High availability
  • Federation
  • Hands On
  • Architecture
  • Scheduler
  • Resource Manager
  • Yarn Hands On
  • Types of installation (standalone, distributed)
  • Hadoop distributions (Apache, cloudera and hortonworks)
  • Setup linux for Hadoop installation (Java and SSH)
  • Haddop directory structure
  • XML, masters and slave files
  • Checking system health
  • Checking file system health
  • Block size, replication factor and block health monitoring
  • Benchmarking cluster
  • Hands On
  • Superuser
  • Authorization
  • Secure Mode
  • Adding and de-commissioning nodes
  • Secondary NameNode
  • Failover
  • Manage Quotas
  • Enabling Thrash
  • Hands On
  • Hadoop infrastructure monitoring
  • Hadoop specific monitoring
  • Install and configure Nagios / Ganglia
  • Capture metrics
  • Hands on
  • Discuss Hive, Sqoop, Pig, HBase, Flume
  • Use cases of each
  • Use Hadoop streaming to write code in Perl /
  • Python
  • Hands on

BIG DATA HADOOP DEV

  • Understand What Is Big Data
  • Analyze Limitations And Solutions Of Existing Data Analytics Architecture
  • Understand What Is Hadoop And Its Features
  • Hadoop Ecosystem
  • Understand Hadoop 2.x Components
  • Perform Read And Write In Hadoop
  • Understand Rack Awareness Concept
  • Run Hadoop In Different Cluster Nodes
  • Implement Basic Hadoop Commands On Terminal
  • Prepare Hadoop 2 Configuration Files Analyze The Parameters In It.
  • Implement Password-less Ssh On Hadoop Cluster
  • Analyze Dump Of A Mapreduce Program
  • Implement Different Data Loading Techniques
  • Analyze Different Use-cases Where Mapreduce Is Used
  • Differentiate Between Traditional Way And Mapreduce Way
  • Learn About Hadoop 2.x Mapreduce Architecture And Components
  • Understand Execution Flow Of Yarn Mapreduce Application
  • Implement Basic Mapreduce Concepts
  • Run A Mapreduce Program
  • Analyze Mapreduce Job Submission Flow
  • Implement Combiner And Partitioner In Mapreduce
  • Understand Mapreduce Codes In Details
  • Code In Mapreduce For A Given Problem Statement
  • Understand Input Splits Concepts In Mapreduce
  • Module 5– Advance Mapreduce
  • Implement Counter In Mapreduce
  • Numerical Summarizations
  • Counting With Counters
  • Top K Records
  • Distinct Records
  • Total Order Sorting
  • Reduce Side Join
  • Replicated Join
  • Implement Distributed Cache Concept In Mapreduce
  • Customizing Input And Output In Hadoop
  • Implement Custom Input Format In Mapreduce
  • Implement Sequence Input Format In Mapreduce
  • Implement Xml Input Format In Mapreduce
  • Pig And Its Need
  • Difference Between Pig And Mapreduce
  • Pig Features And Programming Structure
  • Pig Running Modes
  • Pig Components And Data Models
  • Basics Operations In Pig
  • Udf In Pig
  • Hive And Its Use Cases
  • Hive Vs. Pig
  • Hive Architecture And Components
  • Primitive And Complex Type In Hive
  • Data Models In Hive
  • Query Efficiency Measures
  • Partitioning
  • Bucketing
  • Hive Script And Hive Udf
  • Implement Flume Job To Download Data From Twitter
  • Implement Flume Job To Download Data From Other Sources
  • Implement Sqoop To Import Table From Rdbms Into Hdfs.
  • Implement Sqoop To Import All Tables From Rdbms Into Hdfs.
  • Implement Sqoop To Import Table From Rdbms Into Hive.
  • Implement Sqoop To Import Schema And Tables Details Rdbms.
  • Implement Sqoop To Export Data To Rdbms (insert And Update Mode)
  • Implement Sqoop To Generate Java Classes Which Encapsulate And Interpret Imported Records
  • Understand Oozie
  • Schedule Job In Oozie
  • Implement Oozie Workflow
  • Implement Oozie Coordinator

HADOOP 6 WEEKS TRAINING

  • Architecture of Java(JDK/JRE/JVM)
  • Data Types Variables in Java.
  • Type Casting.
  • OOPS Concept.
  • Polymorphism
  • Abstraction
  • Inheritance
  • Encapsulation
  • Control Structures.
  • Do-While Loop
  • For Loop
  • If-Else
  • Switch Case
  • Exception Handling.
  • Collection Framework.
  • Map/Set/Tree
  • What is Master Slave Architecture of Hadoop.
  • Distributed Computing and Parallel Processing.
  • Replication Factors and Heart Beat in Architecture.
  • Implement Basic Hadoop Commands on Terminal.
  • Analyze Different use cases Where MapReduce is Used.
  • Differentiate Between Traditional way and MapReduce way.
  • Map Phase and Reduce Phase.
  • Understand execution Flow of YARN MapReduce Application.
  • Run A MapReduce Program(Word-Count)
  • Architecture and its Component.
  • Data Models In Hive.
  • Query Efficiency Measures.
  • Types of Tables in Hive.
  • Loading Data into Hive Tables(Local HDFS Mode)
  • Partitioning and Bucketing
  • Hive Scripts.
  • What is Sqoop?
  • How to Import and Export Data Using Sqoop.
  • How we transfer data from RDBMS to HDFS and Vice Versa.
  • What is HBase?
  • Architecture of HBase.
  • Columns and Column Family.
  • Key-Value Pairing of Data into HBase.
  • Basic Commands of HBase.

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