MapReduce Online Training | MapReduce Certification Course | Edureka

MapReduce Design Patterns Certification Training

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


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Why this course ?

  • Companies like AOL, EBay and Twitter use MapReduce Design Patterns
  • At Google, MapReduce Design Patterns was used to completely regenerate Google's index of the World Wide Web
  • ​The average pay stands at $189K USD P.A - ​Indeed.com​​
  • 675 + satisfied learners. Reviews

Online self - paced learning

Online Self Learning Courses are designed for self-directed training, allowing participants to begin at their convenience with structured training and review exercises to reinforce learning. You'll learn through videos, PPTs and complete assignments, projects and other activities designed to enhance learning outcomes, all at times that are most convenient to you.
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Train your employees with exclusive batches and offers and track your employee's progress with our weekly progress report.

Course Duration

You will undergo self-paced learning where you will get an in-depth knowledge of various concepts that will be covered in the course.

Real-life Case Studies

Towards the end of the Course, you will work on a real life case study.

Assignments

Each class has practical assignments which shall be finished before the next class and helps you to apply the concepts taught during the class.

Lifetime Access

You get lifetime access to Learning Management System (LMS) where presentations, quizzes, installation guide & class recordings are there.

Certification

Edureka certifies you as a MapReduce Design Pattern Expert based on the project reviewed by our expert panel.

Forum

We have a community forum for all our customers that further facilitates learning through peer interaction and knowledge sharing.

MapReduce Design Patterns course takes the MapReduce developers on the path of writing MapReduce code as experts would, using well established Design Patterns.The concepts like Shuffling Pattern, Description, Applicability, Structure (how mappers, combiners & reducers are used in this pattern), use cases, analogies to Pig & SLQ, Performance Analysis, and how to apply MapReduce to real world use cases will be covered in the course.

After the completion of the MapReduce Design Patterns course, you will be able to:

1. Understand about the commonly used Design Patterns in MapReduce 

2. Learn the scenarios where to apply those Patterns in real world problems 

3. Write mature code using MapReduce 

4. Learn the best practices for using MapReduce

The course is designed for people who want to gain expertise in their understanding of MapReduce paradigm.

The pre-requisites for this course include hands-on experience in Hadoop Framework and a basic understanding of MapReduce.

Design Patterns are problem specific templates developers have perfected over the years for writing correct and efficient codes. It encodes correct practices for solving a given piece of problem, so that a developer need not re-invent the wheel. MapReduce program bugs can be hard to debug – using well established Design Patterns can alleviate the pain.

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 are Design Patterns 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.     

. Call a Course Adviser for discussing Curriculum Details . 1844 230 6361
The project work will consist of 5 different components based on different MapReduce Design Patterns learnt during the duration of the course. Participants are expected to complete each of these components in their spare time between the weekly classes. Each of these components will require close to 3 hours to complete. Solution to the project will be discussed in the last module. 
For your practical work, we will help you setup Edureka's Virtual Machine in your System. This will be a local access for you. The required installation guide is present in LMS.
We do provide placement assistance by routing relevant job opportunities to you as and when they come up. To get notified on relevant opportunities, it is important that you fill out your profile details.

It is important to attend classes and complete assignments. Course completion is an important criterion based on which we screen profiles of learners interested in a particular job. Also, before your profile is shared with prospective employers, you will have to go through an internal assessment by edureka. So it is important to be well versed with the course concepts to become eligible for placement opportunities.
All our instructors are working professionals from the Industry and have at least 10-12 yrs of relevant experience in various domains. They are subject matter experts and are trained by Edureka for providing online training so that participants get a great learning experience.
You can give us a CALL at +91 88808 62004/1800 275 9730 (US Tollfree Number) OR email at sales@edureka.co

At the end of your course, you will work on a real time Project. You will receive a Problem Statement along with a dataset to work. Once you are successfully through with the project (reviewed by an expert), you will be awarded a certificate with a performance based grading. 

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MapReduce Design Patterns Certification Training