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
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.
Learn Big Data Live Online from top industry professionals
Interactions with an Live Expert, get your doubts cleared in Real Time.
Access to World Class Instructors, from anywhere
Your guide from Edureka, to ensure you achieve your learning goals.
Live course assures 6 times more probability of getting certified
Big Data Hadoop Certification Training
Python Spark Certification Training usin...
Apache Spark and Scala Certification Tra...
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.
I'm currently enrolled in a lot of courses offered at Edureka, so I'm attending live classes and using their study materials, course projects, have access to support staff and teachers. I can confidently say Edureka staff is very hard working and committed to help students which is reliable. Course instructors are experienced candidates from industry who know what they are teaching. Course materials are pretty comprehensive and students need to work very hard to finish course projects, pass the project interviews and gain certification. I say, it is worth it.
Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best.
Edureka aptly named, gives the students a Eureka" Moment during the course. Learning is a world to explore and Edureka provides us with the navigation maps. I never for a minute felt that I am doing this course online away from the faculty and the staff."
I have been subscribing to Edureka's courses for almost a year now, primarily related to Big Data and Data analytics. These courses have helped me to gain that competitive edge which is required at the job. Also, their courses cover a breadth of topics and range from computer programming languages like Java to Data Visualisation. There is also constant updation done on these courses, and you can talk to their support staff at any time for any assistance. I found the faculties very knowledgeable, and all the courses that I enrolled in were delivered in a very detailed and professional manner. For any person looking for online training, I can recommend Edureka without hesitation.
I have taken Informatica, Hadoop, R-programming, Spark and Scala and several other training's from past 3 years. There is no way to say that these courses are bad.. this is the exceptional institute with so many senior people who spend lot of their efforts for a cause. Because i know the pain as a trainer as well. Hats off to to team and the person who started edureka. I'm posting my personal experience and i do lot of social service. Good luck to others.
I am thankful to Edureka which is one of the best Educational organization. I have undergone two highly rated courses (Big data and Hadoop, Spark and Scala). Now i am doing well with the stuff learnt, after getting certified for big data and hadoop, I'm getting many offers from many companies. After the great experience of learning hadoop technology, I am now keen to enroll for Data science course. I hope i get the same learning experience which i got while undergoing my previous courses. I heartily thank edureka for helping me to make my career. The overall team [Trainers, support team, online support team] is the best.