Map Reduce Design Patterns Certification Training

MapReduce Design Patterns Certification Training

A self-paced course designed by Hadoop Experts to provide the knowledge and skills in the field of MapReduce Framework and help you to solve the use cases by using MapReduce concepts.

MapReduce originally referred to the proprietary Google technology.

Companies like Twitter, LinkedIn, AOL, EBay and Alibaba use MapReduce.

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Mapreduce Design Patterns UpComing Batches

Nov-29 - Jan-10

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Dec-11 - Jan-22

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Dec-18 - Jan-29

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Dec-25 - Feb-05

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Jan-01 - Feb-12

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Timings: 20:30 PM To 23:30 PM (IST)

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Course Curriculum

MapReduce Design Patterns Certification Training

SELF PACED

OL Tech Edu's Write MapReduce code using design patterns, learn pattern shuffling, applicability, analogies to Pig & SLQ, Performance Analysis, etc.

  • WEEK 5-6
  • 10 Modules
  • 6 Hours
Safe Paced

Learning Objectives - In this module, you will be introduced to Design Patterns vis-a-vis MapReduce, general structure of the course & project work. Also, discussion on Summarization Patterns: Patterns that give a summarized top level view of large data sets.

Topics:

  • Review of MapReduce.
  • Why Design Patterns are required for MapReduce?
  • Discussion of different classes of Design Patterns.
  • Discussion of project work and problem, About Summarization Patterns.
  • Types of Summarization Patterns – Numerical Summarization Patterns.
  • Inverted Index Pattern and Counting with counters pattern.
  • Description.
  • Applicability.
  • Structure (how mappers, combiners & reducers are used in this pattern), use cases, analogies to Pig & SLQ, Performance Analysis.
  • Example code walk-through & data flow.

Learning Objectives - In this module, we will discuss about Filtering Patterns: Patterns that create subsets of data for a more detailed view. 

Topics:

  • About Filtering Patterns
  • Explain & Distinguish 4 different types of Filtering Patterns:
  • Filtering Pattern.
  • Bloom Filter Pattern.
  • Top Ten Pattern and Distinct Pattern.
  • Description, Applicability, Structure (how mappers, combiners & reducers are used in this pattern), use cases, analogies to Pig & SLQ, Performance Analysis.
  • Example code walk-through & data flow.


Learning Objectives - In this module, we will discuss about Data Organization Patterns: Patterns that are about re-organizing and transforming data. Categories of these patterns are used together to achieve end objective.

Topics:

  • About Organization Patterns.
  • Explain 5 Different Types of Organization Patterns:
  • Structured to Hierarchical Pattern.
  • Partitioning Pattern.
  • Binning Pattern.
  • Total Order Sorting Pattern and Shuffling Pattern.
  • Description.
  • Applicability.
  • Structure (how mappers, combiners & reducers are used in this pattern), use cases, analogies to Pig & SLQ, Performance Analysis.
  • Example code walk-through & data flow.


Learning Objectives - In this module, we will discuss Join Patterns: Patterns to be used when your data is scattered across multiple sources and you want to uncover interesting relationships using these sources together.

Topics:

  • About Join Patterns.
  • Explain 4 different types of Join Patterns:
  • Reduce Side Join Pattern.
  • Replicated Join Pattern.
  • Composite Join Pattern.
  • Cartesian Product Join Pattern.
  • Description.
  • Applicability.
  • Structure (how mappers, combiners & reducers are used in this pattern), use cases, analogies to Pig & SLQ, Performance Analysis.
  • Example code walk-through & data flow.
  •   



Learning Objectives - In this module, we will discuss about Meta Patterns & Graph Patterns. Meta Patterns are different from other Patterns discussed above i.e. these are not basic patterns, but Pattern about Patterns, Introduction to Graph Patterns.  

Topics:

  • About Meta Patterns.
  • Types of Meta Patterns:
  • Job Chaining – Description, use cases, chaining with  driver, basic & parallel job chaining, chaining with shell scripts, chaining with job control, Example code walk-through.
  • Chain Folding – Description, What to fold, Chain mapper, Chain Reducer.
  • Example code walk-through.
  • Job Merging - Description, Steps for merging two jobs,  Example code walk-through, Introduction to Graph design Pattern.
  • Types of Graph Design Patterns: In-mapper Combining Pattern, Schimmy Pattern and Range Partitioning Pattern.
  • Pseudo-code for each pattern applied to Page-rank algorithm.



Learning Objectives - In this module, we discuss about Input Output Pattern: Input Output Patterns are about customizing input & output to increase the value of map reduce, Project Review.  

Topics:

  • About Input Output Patterns.
  • Types of Input Output Patterns .
  • Customizing Input & Output.
  • Generating Data.
  • External Source output, External Source Input.
  • Partition Pruning: Description.
  • Applicability.
  • Structure (how mappers, combiners & reducers are used in this pattern), use cases, analogies to Pig & SLQ.
  • Performance Analysis.
  • Example code walk-through & reviewing the project work solution.


Program Syllabus

Curriculum

You can also view the program syllabus by downloading this program Curriculum.

Projects

How will I execute the practicals?

For your practical work, you will setup OL Tech Edu Virtual Machine in your System. This will be a local access for you. The required installation guide is present in LMS.

Which Case-Studies will be part of this course

Project 1: Analysing Aadhar Card Data-Industry: Government Sector-Problem Statement:- Below are few of the problem statements that we have chosen to work on this data set:- 1.Find out the total number of cards approved by states. 2. Find out the total number of cards rejected by states.

Course Description


About The Course
About the Course.
  • MapReduce generates and processes big data on a cluster of computers using a parallel, distributed algorithm, providing for fault tolerance.
  • If Hadoop is the nervous system connecting many different servers together, then MapReduce is the brain generating and processing information into meaningful data.
  • It works through two basic procedures: mapping and reducing. First it maps the data, filtering and sorting it, breaking it down into key/value pairs. Then, the Reduce operation takes this data and carries out a summary, combining the pairs into smaller ones and, in the process, rendering the information more meaningful. In between these two operations, MapReduce carries out a shuffle operation that redistributes all data belonging to a key to the same node as the key.
  • Thus network communication is dramatically reduced and, with it, the cost of operation.

What Are The Course Objectives
What are the Course Objectives?
  • The main objective of MapReduce online training program is to teach participants how to write MapReduce programs, moving from simple to complex programs by degrees and with plenty of examples.
  • It begins with the MapReduce installation and configuration process and moves on to theory & practice. Describing the data flow of the Mapper input and Reducer output processing, it provides the big picture regarding the concepts involved in MapReduce. It talks about how the program manages servers and how to interact with the MapReduce API. It shows how to manage jobs with the Hadoop CLI and monitor them with MCS.
  • It discusses how to work with different data sources on MapReduce and the distributed cache. It also includes the techniques involved in running multiple jobs and chaining.

Who Should Go For This Course
Who should go for this course?
  • It is recommended that the participants have some, rudimentary proficiency in Core Java and SQL.
  • However, there are no formal prerequisites for the course and anyone may register for MapReduce online training program.

What Are The Pre Requisites For This Course
What are the pre-requisites for this course?Participants are expected to have:
  • As the number of organizations trying to harness the power of big-data processing multiplies, IT departments are finding out that their mainframes, designed for structured data, are not able to handle the flood of unstructured data.
  • This fact is prompting a shift to up-to-date platforms that can handle enterprise workloads, which is where MapReduce and Hadoop come in.
  • MapReduce and Hadoop offer IT departments the ability to handle unlimited concurrent tasks and enormous processing power with efficiency and stability.
  • These are the tools that are necessary for crunching high volumes of unstructured data.
  • Thus, developers, data architects, database administrators, and data analysts, in particular, have much to gain by mastering MapReduce.
  • The average salary of Hadoop developers working in the big data industry has been reported as $135,000.
  • Take advantage of this global shift to big data and enroll in MapReduce online training program today.

Course Certification

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Features

Explore step by step paths to get started on your journey to Jobs of Today and Tomorrow.

Instructor-led Sessions

30 Hours of Online Live Instructor-Led Classes.
Weekend Class : 10 sessions of 3 hours each.

Real Life Case Studies

Real-life Case Studies

Live project based on any of the selected use cases, involving implementation of the various real life solutions / services.

Assignments

Assignments

Each class will be followed by practical assignments.

24 x 7 Expert Support

24 x 7 Expert Support

We have 24x7 online support team to resolve all your technical queries, through ticket based tracking system, for the lifetime.

Certification

Certification

Towards the end of the course, OL Tech Edu certifies you for the course you had enrolled for based on the project you submit.

Course FAQ's

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