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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
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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.
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Edureka has very good instructors and technical support team, I have completed the course BIG DATA & AWS through web training, and it was very good training, helped me to learn very swiftly. When I stuck up in installation and in writing map reduce programming technical support resolved very quickly and I was very happy with their response in time. As well as they providing life time access for our completed course videos so it will be useful in real time projects. I feel very happy to recommend Edureka for my friends on big data course.
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