Comprehensive mapreduce certification training

Comprehensive mapreduce certification training
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About the Comprehensive MapReduce Certification Training

A self-paced course designed by Hadoop Experts to provide the knowledge and skills in the field of MapReduce Framework and help you to solve the use cases by using MapReduce concepts.MapReduce is the underlying engine of Hadoop. The self-paced Comprehensive MapReduce course is designed for the learners to understand and implement various frameworks of MapReduce.

About ProICT

Who are we? ProICT LLC, is a registered online training provider found and led by the group of IT working professionals and experts. Our trainers are not only highly experienced and knowledgeable but also current IT working Professionals leading IT companies in USA, UK, Canada and other countries. We are ready to share our knowledge and years  of working experience with other professionals to assist and guide them  get ahead in career.


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.     

MapReduce training

Zeolearn academy brings a comprehensive MapReduce course that will introduce you to distributed data processing and using MapReduce to process large amounts of data. With more focus on practical and hands-on exercises, the workshop will teach you to write complex MapReduce programs and program in YARN. Understanding the advanced features of MapReduce will help you use it to give logical insights for business benefits.

The emergence of Big Data has alleviated the need for large scale parallel processing of data. MapReduce is a software framework that helps perform this processing in a scalable and fault tolerant manner. Developed by Google, MapReduce is already powering the web searches of top companies such as Yahoo. Being a MapReduce programmer can greatly enhance your career prospects. By enrolling into this course, you will develop your knowledge and skills in the MapReduce framework and the  MapReduce certification that you receive upon successful completion of course proves your ability to the employers. We also offer free reference materials to the registered candidates at our institute.

Here’s what you will learn from our coaching!

  • About Hadoop and its use in parallel data processing
  • To write MapReduce programs to analyse Big data and bring in business benefits to the organization
  • The advanced features of MapReduce and YARN

Is this course right for you?

This MapReduce online course is apt for Software Professionals, Java Developers, Analytics Professionals, ETL developers, Project Managers, Testing and other professionals who are keen to pursue a career in Big Data analytics.

Prerequisites:

 

Participants need to have hands-on experience in Core Java and good analytical skills to grasp the concepts explained in this course.

The project work will consist of 5 different components based on different MapReduce Design Patterns learnt during the duration of the course. Participants are expected to complete each of these components in their spare time between the weekly classes. Each of these components will require close to 3 hours to complete. Solution to the project will be discussed in the last module. 
For your practical work, we will help you setup Edureka's Virtual Machine in your System. This will be a local access for you. The required installation guide is present in LMS.

How soon after Signing up would I get access to the Learning Content?
As soon as you enroll in the course, your LMS (The Learning Management System) access will be functional. You will immediately get access to our course content in the form of a complete set of Videos, PPTs, PDFs, and Assignments. You can start learning right away.

What are the payment options?
For USD payment, you can pay by Paypal. 

What if I have more queries?
You can give us a CALL at +1 718-715-0914