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.     

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.