MapReduce Design Pattern Certification Training
Access Duration - 365 Days
Awarded By Edureka
Guided Learning Hours (20)
Course Material
Self-study Online
4.5( 2 REVIEWS )
126 STUDENTS
What Will I Learn?
Gain an introduction to Data Patterns
Learn about summarised view of large data sets
Learn patterns that are about re-organising and transforming data
Learn about different types of organisation patterns
Understand scenarios where patterns can be applied to achieve better results
Overview
MapReduce Design Pattern Certification Training is designed to provide learners with an introduction to Design Patterns. If you aspire to have a career in Big Data Analytics, this course provides an excellent starting point. The course will focus on the relevance and implementation of MapReduce, how to implement different frameworks of MapReduce, how to use MapReduce and write mature code and avoid common mistakes in the process.
MapReduce Design Pattern Certification Training will provide a wealth of knowledge on the concepts of Applicability, Description, Shuffling Pattern and Structure. Learners will gain an in-depth understanding of the frequency of Design Patterns used in MapReduce. The course will also teach you about Filtering Patterns, Meta and Graph Patterns, Data Organisation Patterns and Input Output Pattern.
MapReduce Design Pattern Certification Training will teach learners to write MapReduce code using established design patterns. Qualifying in this course will set you in the right direction, giving you the opportunity to open the door to new and exciting job roles in data and analytics.
Why You Should Consider Taking this Course at Global Edulink?
Global Edulink is a leading online provider for several accrediting bodies, and provides learners the opportunity to take this exclusive course awarded by Edureka. At Global Edulink, we give our fullest attention to our learners’ needs and ensure they have the necessary information required to proceed with the Course. Learners who register will be given excellent support, discounts for future purchases and be eligible for a TOTUM Discount card and Student ID card with amazing offers and access to retail stores, the library, cinemas, gym memberships and their favourite restaurants.
- Access Duration
- Who is this Course for?
- Entry Requirement
- Method of Assessment
- Certification
- Awarding Body
- Career Path & Progression
The course will be directly delivered to you, and you have 12 months access to the online learning platform from the date you joined the course. The course is self-paced and you can complete it in stages, revisiting the lessons at any time.
This course is recommended for individuals, who want to have a flourishing career in Big Data Analytics,
- Data Analysts
- Learners should be over the age of 16, and have a basic understanding of English, ICT and numeracy
- A sound educational background is recommended
- Learners should have a basic understanding of Hadoop framework
- Basic knowledge of MapReduce is recommended
In order to complete the Course MapReduce Design Pattern Certification Training, learners will have to submit an assignment. The assignment will demonstrate your familiarity with the particular subject and will test your ability to apply it in a real-world scenario.
Upon the successful completion of the course, you will be awarded the MapReduce Design Pattern Certification Training by Edureka.
Edureka is a pioneer in online learning and an agency that has frequently and consistently collaborated with educational and academic institutes. It guarantees expert commitment and is dedicated to learners taking their courses. Edureka is internationally recognised and takes learners a step closer to a leading career in the IT industry.
MapReduce Design Pattern Certification Training will improve your candidature for a number of jobs in data management. You can study further courses in the same field and enhance your academic expertise, and add this as a valuable skillset on your resume. Your skills will be recognised by leading organisations that give you the opportunity to land a well-paying job in data analytics. Given below are job titles you can compete for, along with the average UK salary per annum according to https://www.glassdoor.com.
- Data Analysts - £50,000 (Approximately)
Key Features
Gain an Accredited UK Qualification
Access to Excellent Quality Study Materials
Personalised Learning Experience
Support by Phone, Live Chat, and Email
Eligible for TOTUM Discount Card
UK Register of Learning Providers Reg No : 10053842Course Curriculum
| 1:Introduction & Summarization Patterns | |||
| 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 | |||
| 2: Filtering Patterns | |||
| 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 | |||
| 3: Data Organization Patterns | |||
| 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 | |||
| 4 : Join Patterns | |||
| About Join Patterns | |||
| Explain 4 different types of Join Patterns: Reduce Side 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 | |||
| 5: Meta Patterns & Graph Patterns | |||
| 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 | |||
| 6: Input Output Pattern & Project Review | |||
| 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 | |||
Lydia Mccarthy
The course provided great content on learning best practices in MapReduce for developers like myself. I was recommended this course by my employer and I’m completely satisfied with the outcome.
Ernest Jones
The video series was well presented on writing MapReduce code and establishing Design Patterns. The course taught me how to apply my knowledge of MapReduce in real-world cases.