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HADOOP COURSE CONTEST
HADOOP:
����‚�š�� What is Big Data?
����‚�š�� What is Hadoop?
����‚�š�� Relation between Big Data and Hadoop.
����‚�š�� What is the need of going ahead with Hadoop?
����‚�š�� Scenarios to apt Hadoop Technology in REAL TIME Projects
����‚�š�� Challenges with Big Data
»Storage
» Processing
����‚�š�� How Hadoop is addressing Big Data Changes
����‚�š�� Comparison with Other Technologies
» RDBMS
» Data Warehouse
» TeraData
����‚�š�� Different Components of Hadoop Echo System
» Storage Components
» Processing Components
HDFS (Hadoop Distributed File System)
����‚�š�� What is a Cluster Environment?
����‚�š�� Cluster Vs Hadoop Cluster.
����‚�š�� Significance of HDFS in Hadoop
����‚�š�� Features of HDFS
����‚�š�� Storage aspects of HDFS
» Block
» How to Configure block size
» Default Vs configurable block size
» Why HDFS block size is so large
» Design Principles of block size
����‚�š�� HDFS Architecture 5 demons of Hadoop
» NameNode and its functionality
» Data node and its functionality
» Job Tracker and its functionality
» Task Track and its functionality
» Secondary Name Node and its functionality
����‚�š�� Replication in Hadoop - Fail Over Mechanism
» Data Storage in Data Nodes
» Fail Over Mechanism in Hadoop - Replication
» Replication Configuration
» Custom Replication
» Design Constraints with Replication Factor
����‚�š�� Accessing HDFS
» CLI (Command Line Interface) and HDFS Commands
» Java Based Approach
����‚�š�� Hadoop Archives
MapReduce
����‚�š�� Why Map Reduce is essential in Hadoop?
����‚�š�� Processing Daemons of Hadoop
» Job Tracker
> Roles Of Job Tracker
> Drawbacks w.r.to Job Tracker failure in Hadoop Cluster
> How to configure Job Tracker in Hadoop Cluster
» Task Tracker
> Roles of Task Tracker
> Drawbacks w.r.to Task Tracker Failure in Hadoop Cluster
����‚�š�� Input Split
» InputSplit
» Need Of Input Split in Map Reduce
» InputSplit Size
» InputSplit Size Vs Block Size
»InputSplit Vs Mappers
����‚�š�� Map Reduce Life Cycle
» Communication Mechanism of Job Tracker and Task Tracker
» Input Format Class
» Record Reader Class
» Success Case Scenarios
» Failure Case Scenario
» Retry Mechanism in Map Reduce
����‚�š�� MapRedue Programming Model
» Different phases of Map Reduce Algorithm
» Different Data types in Map Reduce
> Primitive Data types Vs Map Reduce Data types
» How to write basic Map Reduce Program
> Driver Code
> Mapper Code
> Reducer Code
»Driver Code
> Importance of Driver Code in a Map Reduce program
> How to Identify the Driver Code in Map Reduce program
> Different sections of Driver code
» Mapper Code
> Importance of Mapper Phase in Map Reduce
> How to Write a Mapper Class?
> Methods in Mapper Class
» Reducer Code
> Importance of Reduce phase in Map Reduce
> How to Write Reducer Class?
> Methods in Reducer Class
» IDENTITY MAPPER & IDENTITY REDUCER
» Input Formats in Map Reduce
> TextInputFormat
> KeyValueTextInputFormat
> NLinelnputFormat
> DBlnputFormat
> SequenceFilelnputFormat
> How to use the specific input format in Map Reduce
» Output Formats in Map Reduce
> TextOutputFormat
> KeyValueTextOutputFormat
> NLineOutputFormat
> DBOutputFormat
> SequenceFileOutputFormat
> How to use the specific Output format in Map reduce
» Map Reduce API(Application Programming
> New API
> Deprecated API
» Combiner in Map Reduce
> Is combiner mandate in Map Reduce
> How to use the combiner class in Map Reduce
> Performance tradeoffs w.r.to Combiner
» Partitioner in Map Reduce
> Importance of Pratitioner class in Map Reduce
> How to use the Partitioner class in Map Reduce
> hashPartitioner functionality
> How to write a custom Partitioner
» Compression techniques in Map Reduce
> Importance of Compression in Map Reduce
> What is CODEC
> Compression Types
> GzipCodec
> BzipCodec
> LZOCodec
> SnappyCodec
> Configurations w.r.to Compression Techniques
> How to customize the Compression per one job Vs all the job
» Joins - in Map Reduce
> Map Side Join
> Reduce Side Join
> Performance Trade Off
> Distributed cache
» How to debug MapReduce Jobs in Local and Pseudo cluster Mode.
» Introduction to MapReduce Streaming
» Data localization in Map Reduce
Apache PIG
����‚�š�� Introduction to Apache Pig
����‚�š�� Map Reduce Vs Apache Pig
����‚�š�� SQL Vs Apache Pig
����‚�š�� Different data types in Pig
����‚�š�� Modes Of Execution in Pig
» Local Mode
» Map Reduce OR Distributed Mode
����‚�š�� Execution Mechanism
» Grunt Shell
» Script
����‚�š�� Embedded
����‚�š�� Transformations in Pig
����‚�š�� How to write a simple pig script
����‚�š�� How to develop the Complex Pig Script
����‚�š�� Bags, Tuples and fields in PIG
����‚�š�� UDFs in Pig
» Need of using UDFs in PIG
» How to use UDFs
» REGISTER Key word in PIG
����‚�š�� When to use Map Reduce & Apace PIG in REAL TIME Projects
HIVE
����‚�š�� Hive Introduction
����‚�š�� Need of Apache HIVE in Hadoop
����‚�š�� Hive Architect
» Driver
» Compiler
» Executor(Semantic Analyzer)
����‚�š�� Meta Store in Hive
» Importance Of Hive Meta Store
» Embedded metastore configuration
» External metastore configuration
» Communication mechanism with Metastore
����‚�š�� Hive Integration with Hadoop
����‚�š�� Hive Query Language(Hive QL)
����‚�š�� Configuring Hive with MySQL MetaStore
����‚�š�� SQL VS Hive QL
����‚�š�� Data Slicing Mechanisms
» Partitions In Hive
» Buckets In Hive
» Partitioning Vs Bucketing
» Real Time Use Cases
����‚�š�� Collection Data Types in HIVE
» Array
» Struct
» Map
����‚�š�� User Defined Functions(UDFs) in HIVE
» UDFs
» UDAFs
» UDTFs
» Need of UDFs in HIVE
����‚�š�� Hive Serializer/Deserializer SerDe
����‚�š�� HIVE - HBASE Integration
SQOOP
����‚�š�� Introduction to sqoop
����‚�š�� MySQL client and Server Installation
����‚�š�� How to connect to Relational database using Sqoop
����‚�š�� different Sqoop Commands
» Different flavours of Improrts
» Export
» Hive-Imports
HBASE
����‚�š�� HBASE Introduction
����‚�š�� HDFS Vs HBase
����‚�š�� Hbase use cases
����‚�š�� Hbase basics
» Column families
» Scans
����‚�š�� HBase Architecture
����‚�š�� Clients
» REST
» Thrift
» Java Based
» Avro
����‚�š�� Map Reduce Integration
����‚�š�� Map Reduce over HBase
����‚�š�� HBase Admin
» Schema Definition
» Basic CRUD Operations
FLUME
����‚�š�� Flume Introduction
����‚�š�� Flume Architecture
����‚�š�� Flume Master, Flume Collector and Flume Agent
����‚�š�� Flume Configurations
����‚�š�� Real Time Use Case using Apache Flume
OOZIE:
����‚�š�� Oozie Introduction
����‚�š�� Oozie Architecture
����‚�š�� Oozie Configuration Files
����‚�š�� Oozie Job Submission
» Workflow.xml
» Coordinatot:xml
» job.coordinatonproperties
YARN (Yet Another Resource Negotiator) - Next Gen. Map Reduce
����‚�š�� What is YARN?
����‚�š�� YARN Architecture
» Resource Manager
» Application Master
» Node Manager
����‚�š�� When should we go ahead with YARN
����‚�š�� Classic Map Reduce Vs YARN Map Reduce
����‚�š�� Different Configuration Files for YARN
IMPALA:
����‚�š�� What is Impala?
����‚�š�� How can we use Impala for Query Processing
����‚�š�� When should we go ahead with Impala
����‚�š�� HIVE Vs Impala
����‚�š�� REAL TIME Use Case with Impala
MongoDB (NoSQL Database):
����‚�š�� Need of NoSQL Databases
����‚�š�� Relational Vs Non-Relational Databases
����‚�š�� Introduction to MongoDB
����‚�š�� Features of MongoDB
����‚�š�� Installation of MongoDB
����‚�š�� Mongo DB Basic operations
OVERVIEW OF KAFKA:
����‚�š�� Topics.
����‚�š�� Producers
����‚�š�� Consumers.
����‚�š�� Brokers.
OVERVIEW OF SPARK:
����‚�š�� Scala
����‚�š�� Spark SQL
����‚�š�� Joins using spark SQL.
HADOOP ADMINISTRATION:
����‚�š�� Hadoop Single Node Cluster Set Up (Hands on Installation on Laptops)
» Operating System Installation
» JDK Installation
» SSH Configuration
» Dedicated Group & User Creation
» Hadoop Installation
» Different Configuration Files Setting
» Name node format
» Starting the Hadoop Daemons
����‚�š�� Multi Node Hadoop Cluster Set Up (Hands on Installation on Laptops) )
» Network related settings
» Hosts Configuration
» Password less SSH Communication
» Hadoop Installation
» Configuration Files Setting
» Name Node Format
» Starting the Hadoop Daemons
����‚�š�� PIG Installation (Hands on Installation on Laptops)
» Local Mode » Clustered Mode » Bashrc file configuration
����‚�š�� SQOOP Installation (Hands on Installation on Laptops)
» Sqoop installation with MySQL Client
����‚�š�� HIVE Installation (Hands on Installation on Laptops)
» Local Mode » Clustered Mode
����‚�š�� Hbase Installation (Hands on Installation on Laptops)
» Local Mode » Clustered Mode
����‚�š�� OOZIE Installation (Hands on Installation on Laptops)
����‚�š�� Mongo DB Installation (Hands on Installation on Laptops)
����‚�š�� Commissioning of Nodes In Hadoop Cluster
����‚�š�� Decommissioning of Nodes from Hadoop Cluster
Offerings:
����‚�š�� Proof Of Concepts (POCs) - En End Execution and demonstration by POC Groups.
����‚�š�� Hand Written Hard Copy & Soft Copy Materials for all the Components.
����‚�š�� Detailed Assistance in RESUME Preparation with Real Time Projects based on your technical back ground.
����‚�š�� Guidance in Resume preparation.
����‚�š�� All the Real Time interview Questions will be provided.
����‚�š�� Linux and SQL concepts will be covered as part of the course.
����‚�š�� Discussing the Interview Questions on a daily basis