Professional certification in Big Data

MAPREDUCE PROGRAMMING MODEL TRAINING

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Duration & Study Mode:
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Location:
London, Flexible online
Duration: 5 Days / 5 Weeks

MapReduce is a component of the Hadoop ecosystem that allows you to precisely break down large amounts of data to achieve your goals. The most popular Hadoop framework – MapReduce – teaches you how to divide a large amount of data into small pieces, which allows you to easily plan the data structure and solve data problems in terms of parallel tasks.

The MapReduce Programming Model course will introduce participants to MapReduce in a structured manner. Participants will understand the various phases of data, such as the map, sort, and reduce phases, after completing this course. There is no single solution to any problem. This course also teaches various MapReduce design patterns.

Prerequisites

  • This MapReduce Programming Model training course requires no prior knowledge. Hence, anyone can take this course.

What will you gain after this course

  • Learn how to use Hadoop to deal with large amounts of data.
  • Acquaint yourself with the art of data breakdown.
  • Learn about the various MapReduce phases.
  • Know about MapReduce Design Patterns.

Jobs you can get
with a MapReduce Programming Certification

  • MapReduce Expert
  • Skilled Big Data worker
  • Hadoop Developer
  • Hadoop Administrator
  • Big Data Administrator

Corporate Group Training

  • Customized Training
  • Onsite / Virtual
  • Instructor-led Delivery
  • For small to large groups

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Who is this certification for?

  • This course is not restricted to any particular technology or programming language. If you work with data, this training will be useful and will give you a competitive advantage. 


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Small Groups

With small groups of students, our instructors can work closely with each student.

Schedule
Flexible Class Schedules

Our class schedules are flexible on weekdays, weekend, or evenings to suit your schedule.

Instructors
Experienced Instructors

Our instructors follow a modified are personalized approach to engage students during class

Lab-Facilities
Hi-Tech Lab Facilities

Our students can access our lab facilities anytime for practical experience during and after studies.

Syllabus

  • 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 aspects of HDFS
  • HDFS Architecture 5 demons of Hadoop
    • Block
    • How to Configure block size
    • Default Vs configurable block size
    • Why HDFS block size is so large
    • Design Principles of block size
    • NameNode and its functionality
    • Data node and its functionality
    • Job Tracker and its functionality
  • Why Map Reduce is essential in Hadoop?
  • Processing Daemons of Hadoop
  • Input Split
  • Map Reduce Life Cycle
  • MapRedue Programming Model
    • 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
    • InputSplit
    • Need Of Input Split in Map Reduce
    • InputSplit Size
    • InputSplit Size Vs Block Size
    • InputSplit Vs Mappers
    • 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
    • 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 Format's in Map Reduce
      • TextInputFormat
      • KeyValueTextInputFormat
      • NLinelnputFormat
      • DBlnputFormat
      • SequenceFilelnputFormat
      • How to use the specific input format in Map Reduce
    • Output Format's 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
    • Task Track and its functionality
    • Secondary Name Node and its functionality
  • Replication in Hadoop - Fail Over Mechanism
  • Accessing HDFS
    • Data Storage in Data Nodes
    • Fail Over Mechanism in Hadoop - Replication
    • Replication Configuration
    • Design Constraints with Replication Factor
    • CLI (Command Line Interface) and HDFS Commands
    • Java Based Approach
  • Hadoop Archives
  • Apache PIG
  • Introduction to Apache Pig
  • SQL Vs Apache Pig
  • Different data types in Pig
  • Modes Of Execution in Pig
  • Execution Mechanism
    • Local Mode
    • Map Reduce OR Distributed Mode
    • Grunt Shell
  • Embedded
  • Transformations in Pig
  • How to write a simple pig script
  • Bags, Tuples and fields in PIG
  • UDFs in Pig
    • Need of using UDFs in PIG
    • How to use UDFs
    • REGISTER Key word in PIG
  • When to use Map Reduce & Apace PIG in REAL TIME Projects
  • Hive Introduction
  • Need of Apache HIVE in Hadoop
  • Hive Architect
  • Meta Store in Hive
    • Driver
    • Compiler
    • Executor(Semantic Analyzer)
    • Importance Of Hive Meta Store
    • External metastore configuration
    • Communication mechanism with Metastore
  • Hive Integration with Hadoop
  • Hive Query Language(Hive QL)
  • Configuring Hive with MySQL MetaStore
  • SQL VS Hive QL
  • Data Slicing Mechanisms
  • Collection Data Types in HIVE
    • Partitions In Hive
    • Buckets In Hive
    • Partitioning Vs Bucketing
    • Real Time Use Cases
    • Array
    • Struct
    • Map
  • User-Defined Functions(UDFs) in HIVE
    • UDFs
    • UDAFs
    • UDTFs
    • Need of UDFs in HIVE
  • Hive Serializer/Deserializer SerDe
  • HIVE - HBASE Integration
  • Introduction to sqoop
  • MySQL client and Server Installation
  • How to connect to Relational database using Sqoop
  • different Sqoop Commands
    • Different flavours of Improrts
    • Export
    • Hive-Imports
  • HBASE Introduction
  • HDFS Vs HBase
  • Hbase use cases
  • Hbase basics
    • Column families
    • Scans
  • HBase Architecture
  • Clients
    • REST
    • Thrift
    • Java Based
    • Avro
  • Map Reduce Integration
  • Map Reduce over HBase
  • HBase Admin
    • Schema Definition
    • Basic CRUD Operations
  • Flume Introduction
  • Flume Architecture
  • Flume Master, Flume Collector and Flume Agent
  • Flume Configurations
  • Real Time Use Case using Apache Flume
  • Oozie Introduction
  • Oozie Architecture
  • Oozie Configuration Files
  • Oozie Job Submission
    • Workflow.xml
    • Coordinatot:xml
    • job.coordinatonproperties
  • What is YARN?
  • YARN Architecture
    • Resource Manager
    • Application Master
    • Node Manager
  • When should we go ahead with YARN
  • Classic Map Reduce Vs YARN Map Reduce
  • Different Configuration Files for YARN
  • What is Impala?
  • How can we use Impala for Query Processing
  • When should we go ahead with Impala
  • HIVE Vs Impala
  • REAL TIME Use Case with Impala
  • Need of NoSQL Databases
  • Relational Vs Non-Relational Databases
  • Introduction to MongoDB
  • Features of MongoDB
  • Installation of MongoDB
  • Mongo DB Basic operations
  • Topics.
  • Producers
  • Consumers.
  • Brokers.
  • Spark SQL
  • Joins using spark SQL.
  • 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
  • Multi Node Hadoop Cluster Set Up (Hands on Installation on Laptops) )
  • PIG Installation (Hands on Installation on Laptops)
  • SQOOP Installation (Hands on Installation on Laptops)
  • HIVE Installation (Hands on Installation on Laptops)
  • Hbase Installation (Hands on Installation on Laptops)
  • OOZIE Installation (Hands on Installation on Laptops)
  • MongoDB Installation (Hands on Installation on Laptops)
  • Commissioning of Nodes In Hadoop Cluster
  • Decommissioning of Nodes from Hadoop Cluster
    • Network related settings
    • Hosts Configuration
    • Password less SSH Communication
    • Hadoop Installation
    • Configuration Files Setting
    • Name Node Format
    • Starting the Hadoop Daemons
    • Local Mode Clustered Mode Bashrc file configuration
    • Sqoop installation with MySQL Client
    • Local Mode Clustered Mode
    • Local Mode Clustered Mode

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