Learning Objectives - This module will expose you to Design Patterns in relation to MapReduce, as well as the overall structure of the course and project work. Also included is a description of Summarization Patterns, which are patterns that provide a summarized top-level perspective of big data sets.

Topics- Examining MapReduce, Why are MapReduce Design Patterns required? Discussion of several Design Pattern courses, Project work and problems are discussed. About Summarization Patterns, Summarization Pattern Types - Numerical Summarization Patterns, Counting using counters and Inverted Index Pattern Description, applicability, structure (how mappers, combiners, and reducers are employed in this pattern), use examples, Pig and SLQ analogies Performance Evaluation, A walkthrough of some code and data flow.

Learning Objectives -  This lesson will go over Filtering Patterns, which are patterns that construct subsets of data for a more thorough perspective. 

Topics - Filtering Patterns, Explain & Distinguish 4 Types of Filtering Patterns: Filtering Pattern, Bloom Filter Pattern, Top Ten Pattern, and Distinct Pattern are all examples of patterns. Description, Applicability, Structure (how mappers, combiners, and reducers are utilized in this design), application cases, Pig and SLQ parallels, Performance Analysis, Example code walk-through, and data flow.

Learning Objectives - We will talk about Data Organization Patterns in this module: Patterns involving data reorganization and transformation. These pattern categories are combined to reach the ultimate goal.

Topics- In terms of organizational patterns, Explain the five types of organizational patterns: structured to hierarchical pattern, partitioning pattern, binning pattern, total order sorting pattern, and shuffle pattern. Description, Applicability, Structure (how mappers, combiners, and reducers are utilized in this design), application cases, Pig and SLQ parallels, Performance Analysis, Example code walk-through, and data flow.

Learning Objectives -This module will go through Join Patterns: Patterns to apply when your data is dispersed across several sources and you want to discover intriguing links by combining these sources.

Topics - In terms of Join Patterns, Explain the following four types of join patterns: Reduced Side Join Pattern, Replicated Join Pattern, Composite Join Pattern, and Cartesian Product Join Pattern. Description, Applicability, Structure (how mappers, combiners, and reducers are utilized in this design), application cases, Pig and SLQ parallels, Performance Analysis, Example code walk-through, and data flow. 

Learning Objectives - This lesson will go over Meta Patterns and Graph Patterns. Meta Patterns differ from the other Patterns listed above in that they are not fundamental patterns, but rather Patterns about Patterns, or an Introduction to Graph Patterns.  

Topics - About Meta Patterns, Meta Pattern Types: Job Chaining - Overview, Use Cases, Basic & Parallel Job Chaining, Chaining with Shell Scripts, Chaining with Job Control Exemplification of code, Chain Folding - What to Fold, How to Fold Chain mapper, Chain Reducer, Walkthrough of Example Code Job Merging - Definition, Procedures for Combining Two Jobs  Exemplification of code, Graph Design Pattern Overview, Patterns of Graph Design: Range Partitioning Pattern, Schimmy Pattern, and In-mapper Combining Pattern Each pattern's pseudocode is applied to the Page-rank algorithm.

Learning Objectives - In this module, we will look at the Input Output Pattern: Customizing input and output to maximize the value of map decrease, Project Review, is what Input Output Patterns are all about.  

Topics -  Partition Pruning: Description, Applicability, Structure (how mappers, combiners, and reducers are used in this pattern), use cases, analogies to Pig and SLQ, Performance Analysis Example code walkthrough and project task solution evaluation.