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Hadoop is a network of open source software that provides a complete framework to deal with storing huge data files and run the application on clusters of commodity hardware. As said Hadoop provides enormous space for data storage, processing power and it also gives the network to knob limitless virtual synchronized tasks. Two computer scientists Doug Cutting and Mike Cafarella in 2006 got inspired by Google’s Map Reduce and designed Hadoop to support data distribution for the search engine. What is Google’s Map Reduce?

Google’s Map Reduce is a software support, which bares all the little sections of broken application. These broken applications can be work on any node in the clump. How to use Hadoop? An outsource software system can help you create a user profile, by querying the user. Once you generate the user profile, compare it with the other profile in reference to its characteristics, to offer relevant recommendations. SAS programming within Hadoop provides a number of techniques, right from matrix factorization to share sift.

Why is Hadoop important? Hadoop posses the ability to store and route enormous amount of data quickly, helps improve Computing power, it is Flexible and the services are low cost. What are the challenges of using Hadoop? A generally recognized talent gap, Data security, fully developed data management and administration. Hadoop Courses in Bangalore

  • what is big data?
  • What is Hadoop?
  • Relation between Big Data and Hadoop.
  • what is the need of going ahead with Hadoop?
  • Scenarios to apt Hadoop Technology in real Time Projects
  • challenges with Big Data
  • Storage
  • Processing
  • How Hadoop is addressing Big Data Changes
  • Comparison With Other Technologies
  • RDBMS
  • Data Warehouse
  • TeraData
  • Different Components of Hadoop Echo System
  • Storage Components
  • Processing Components
  • what is a Cluster Environment ?
  • Cluster Vs Hadoop Cluster.
  • Significance of HDFS in Hadoop
  • Features of HDFS
  • Storage of HDFS
  • Block
  • How to Configure block size
  • Default vs. Configurable Block Size
  • Why HDFS Block Size so large?
  • Design Principles of Block Size

 

  • HDFS Architecture -5 Daemons of Hadoop
  • NameNode and its functionality
  • DataNode and its functionality
  • JobTracker and its functionality
  • TaskTracker and its functionality
  • secondary Name Node and its functionality.

 

  • 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
  • Accessing HDFS
  • CLI(Command Line Interface) and HDFS Commands
  • Java Based Approach

Hadoop Archives

  • Why Map Reduce is essential in Hadoop?
  • 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
  • Role of Task Tracker
  • DrawBacks of w.r.to Task Tracker Failure in Hadoop Cluster
  • Input Split
  • Input Split
  • Need of Input Split in Map Reduce
  • Input Split Size
  • Input Split Size Vs Block Size
  • Input Split Vs Mappers
  • Map Reduce Life Cycle
  • Different Phases of Map Reduce Algorithm
  • Different Data types in Map Reduce
  • Primitive Data Types Vs Map Reduce Data Types
  • How to write a 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
  • Reduce Code
  • Importance of Reducer phase in Map Reduce
  • How to Write Reducer Class?
  • Methods in Reducer class
  • IDENTITY MAPPER &IDENTITY REDUCER

 

  • Input Format’s in Map reduce
  • TextInputFormat
  • Keyvalue TextInputFormat
  • NLineInputFormat
  • DBInputFormat
  • SequenceFileInputFormat
  • How to use the specific input format in Map Reduce
  • Output Formats in Map Reduce
  • TextOutputFormat
  • keyvalue TextOutputFormat
  • NLineOutputFormat
  • DBOutputFormat
  • SequenceFileOutputFormat
  • How to use the specific output format in Map Reduce
  • Map Reduce api(Application Programming Interface )
  • New API
  • Deprecated API
  • Combiner in Map Reduce
  • Is combiner mandate in Map Reduce
  • How to use the cobiner class in Map Reduce
  • Performance tradeoffs w.r.to Combiner

 

  • Partitioner in Map Reduce
  • Importance of Partitioner class in Map Reduce
  • How to use the Partitioner class in Map Reduce
  • hash Partitioner functionality
  • How to Write a custom Partitioner
  • Compression Techniques in Map Reduce
  • Importance of Compression class in Map Reduce
  • What is CODEC
  • Compression Types
  • GzipCodeec
  • BzipCodec
  • LZOCodec
  • SnappuCoedc
  • Configurations w.r.to Compression Techinques
  • How to Customize the Compression per one job Vs all the job
  • Joins -in Map Reduce
  • Map Side Join
  • Reuce Side Join
  • Performance Trade Off
  • Distributed Cache
  • How to debug MapReduce Jobs and Pseudo Cluster mode
  • Introduction to MapReduce Streaming
  • Data localization in Map Reduce
  • Secondary Sorting Using Map Reduce
  • 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 OR Distributed Mode
  • Execution Mechanism
  • Grunt Shell
  • Script
  • Embedded
  • Transformations in pig
  • How to write a simple pig Script
  • How to develop the Complex Pig Script
  • Bags,Tuple and fields in PIG
  • UDFS in Pig
  • Need of using UDF’S IN PIG
  • how to use UDFs
  • Register Key Word in PIG
  • When to use Map Reduce & Apache PIG in REAL Projects
  • Hive Introduction
  • Need of Apache Hive in Hadoop
  • Hive Architecture
  • Driver
  • Complier
  • Executor (Semantic Analyzer)
  • Meta Store in Hive
  • Importance Of Hive Meta Store
  • Embedded meta store configuration
  • External meta store configuration
  • Communication Mechanism with Metastore
  • Hive Integration with Hadoop
  • HIve Query Language(HiveQL)
  • configuring HIve with MySQL MetaStore
  • SQL Vs Hive QL
  • Data Slicing Mechanisms
  • Partitions In Hive
  • Buckets In Hive
  • Partitions Vs Bucketing
  • Real time Use Cases
  • Collection data Types in Hive
  • Array
  • Struct
  • Map
  • User Defined Functions(UDFs) in Hive
  • UDFs
  • UDAFs
  • UDTFs
  • Need of UDFs in Hive
  • Hive Serializer/Deserializer-serDe
  • XPath processing in Hive
  • Hive -Hbase Integration
  • Introduction to Sqoop
  • Mysql client and server Installation
  • How to connect to relational Database
  • Different Sqoop Commands
  • Different flavours of Imports
  • Export
  • Hive-Imports
  • Hbase Introduction
  • Hdfs Vs Hbase
  • Hbase usecases
  • Hbase basics
  • Column families
  • Scans
  • HBase Architecture
  • Clients
  • Rest
  • Thrift
  • Java based
  • Avro
  • Map Reduce Integration
  • Map Reduce over Hbase
  • Schema Definition
  • Basic CRUD Operations
  • Oozie Introduction
  • Oozie Architecture
  • Oozie Configuration Files
  • Oozie job Submission
  • xml
  • xml
  • properties

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