Mapreduce Design Patterns Training

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The Mapreduce design patterns training has been designed for the candidates, who are looking forward to make their career flourish career in the Data Analytics. Focusing on the concepts of the like Applicability, Shuffling Pattern, Description, Structure (how mappers, combiners & reducers are used in this pattern). The candidates would get a better understanding about the frequently used Design Patterns in MapReduce.

Moreover, the candidates would be able to:

  • Develop a better understanding about the frequently used Design Patterns in MapReduce 
  • Know the scenarios where the patterns should be applied for a better result 
  • Learn to write mature code using MapReduce 
  • Learn to practice the use of MapReduce
Target audience

All the working professionals, who are willing to flourish their career in the Big Data Analytics should opt this course.

Prerequisites

The candidates should have prior knowledge of Hadoop Framework and a basic understanding of MapReduce.

The Mapreduce design patterns training has been designed for the candidates, who are looking forward to make their career flourish career in the Data Analytics. Focusing on the concepts of the like Applicability, Shuffling Pattern, Description, Structure (how mappers, combiners & reducers are used in this pattern). The candidates would get a better understanding about the frequently used Design Patterns in MapReduce.

Moreover, the candidates would be able to:

  • Develop a better understanding about the frequently used Design Patterns in MapReduce 
  • Know the scenarios where the patterns should be applied for a better result 
  • Learn to write mature code using MapReduce 
  • Learn to practice the use of MapReduce
Target audience

All the working professionals, who are willing to flourish their career in the Big Data Analytics should opt this course.

Prerequisites

The candidates should have prior knowledge of Hadoop Framework and a basic understanding of MapReduce.

Mapreduce Design Patterns Training Course Content

1. Introduction and Summarization Patterns

  • 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) uses cases analogies to Pig & SLQ Performance Analysis
  • Example code walk-through & data flow.

2. Filtering Patterns

  • About Filtering Patterns
  • Explain and 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

3. Data Organization Patterns

  • 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

4. Join Patterns

  • 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

5. Meta Patterns & Graph Patterns

  • 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.

6. Input Output Pattern & Project Review

  • 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

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