Upcoming Events at Entirety Technology
Sat Jul 20, 12:00 PM - Sat Jul 20, 2:00 PM
Entirety Technology
Daytona Beach, FL 32114.0
Community: Ormond Beach
Description
In this course we will introduce you to distributed data processing, how to use MapReduce to process large amounts of data. This course is focused on providing practical hands-on exercises. Students will learn to write MapReduce programs. Advanced Features of MapReduce
Event Details
In this course we will introduce you to distributed data processing, how to use MapReduce to process large amounts of data. This course is focused on providing practical hands-on exercises. Students will learn to write MapReduce programs. Advanced Features of MapReduce will be covered as well.
Course Schedule
This is a weekend course that will be held July 20 - August 11, 2019 US Pacific Time
The class sessions will be held-Saturday, Sunday every weekend
9:00-11:00 AM US Pacific time, each day.
Please check your local date and time for first session.
Prerequisite
Desired but not required - Exposure to, Working proficiency of Java, sql.
Course Features
4 weeks, 8 sessions, 16 hours of total LIVE Instruction
Training material, instructor handouts and access to useful resources on the cloud provided
Practical Hands on Lab exercises on cloud workstations provided
Actual code and scripts provided
Real-life Scenarios
Course Outline
1. Introduction to MapReduce
MapReduce Overview
MapReduce in Hadoop
History of MapReduce
MapReduce applications
Data Flow in MapReduce
Map and Reduce operations
Job submission flow of MapReduce
Map Operation
Job Initialization
Task Assignment
Job Completion
Job Scheduling
Job Failures
Shuffle and sort
Word Count Problem, Flow and Solution
MapReduce Algorithms
2. Map Reduce Types and Formats
Data Types
File Formats
Input Formats
Output Formats
Explain the Driver, Mapper and Reducer code
Configuring development environment - Eclipse
Writing Unit Test
Running locally
Running on Cluster
3. Understanding MapReduce
Data Flow in MapReduce
MapReduce example
MapReduce Daemons
Job tracker
Task Tracker
Other phases in MapReduce
Data Flow in single, multiple and no reduce task
4. MapReduce with YARN
Hadoop Architecture
Problem with Hadoop 1.x, Hadoop 2.x features,
YARN MapReduce Application Execution Flow
YARN Workflow
Anatomy of MapReduce Program
5. Advanced MapReduce
Counters
Sorting
Input Splits in MapReduce
MapReduce Combiner
MapReduce Partitioner
MapReduce Distributed Cache
MRunit
Reduce Join
Joins - Map Side and Reduce Side
Custom Input Format
Sequence Input Format
Side Data Distribution
Refund Policy
100% refund can be applied if request is initiated 24 hours before the
Course Schedule
This is a weekend course that will be held July 20 - August 11, 2019 US Pacific Time
The class sessions will be held-Saturday, Sunday every weekend
9:00-11:00 AM US Pacific time, each day.
Please check your local date and time for first session.
Prerequisite
Desired but not required - Exposure to, Working proficiency of Java, sql.
Course Features
4 weeks, 8 sessions, 16 hours of total LIVE Instruction
Training material, instructor handouts and access to useful resources on the cloud provided
Practical Hands on Lab exercises on cloud workstations provided
Actual code and scripts provided
Real-life Scenarios
Course Outline
1. Introduction to MapReduce
MapReduce Overview
MapReduce in Hadoop
History of MapReduce
MapReduce applications
Data Flow in MapReduce
Map and Reduce operations
Job submission flow of MapReduce
Map Operation
Job Initialization
Task Assignment
Job Completion
Job Scheduling
Job Failures
Shuffle and sort
Word Count Problem, Flow and Solution
MapReduce Algorithms
2. Map Reduce Types and Formats
Data Types
File Formats
Input Formats
Output Formats
Explain the Driver, Mapper and Reducer code
Configuring development environment - Eclipse
Writing Unit Test
Running locally
Running on Cluster
3. Understanding MapReduce
Data Flow in MapReduce
MapReduce example
MapReduce Daemons
Job tracker
Task Tracker
Other phases in MapReduce
Data Flow in single, multiple and no reduce task
4. MapReduce with YARN
Hadoop Architecture
Problem with Hadoop 1.x, Hadoop 2.x features,
YARN MapReduce Application Execution Flow
YARN Workflow
Anatomy of MapReduce Program
5. Advanced MapReduce
Counters
Sorting
Input Splits in MapReduce
MapReduce Combiner
MapReduce Partitioner
MapReduce Distributed Cache
MRunit
Reduce Join
Joins - Map Side and Reduce Side
Custom Input Format
Sequence Input Format
Side Data Distribution
Refund Policy
100% refund can be applied if request is initiated 24 hours before the
