Online self paced classes

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.

Introduction & Summarization Patterns

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
- 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

Filtering Patterns

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
- 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

Data Organization Patterns

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
- 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

Join Patterns

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

Meta Patterns & Graph Patterns

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
- Range Partitioning Pattern Pseudo-code for each pattern applied to Page-rank algorithm

Input Output Pattern & Project Review

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 & data flow

How will I execute the practicals?

For your practical work, we will help you setup CertAdda’s Virtual Machine in your System. This will be a local access for you. The required installation guide is present in LMS.

Which Case-Studies will be part of 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.

Will I get placement assistance?

To help you in this endeavor, we have added a resume builder tool in your LMS. Now, you will be able to create a winning resume in just 3 easy steps. You will have unlimited access to use these templates across different roles and designations. All you need to do is, log in to your LMS and click on the “create your resume” option.

Who are the Instructor at CertAdda?

All our instructors are working professionals from the Industry and have at least 10-12 yrs of relevant experience in various domains. They are subject matter experts and are trained by CertAdda for providing online training so that participants get a great learning experience.

What if I have more queries?

Just give us a CALL at +91 8178510474 / +91 9967920486 OR email at admin@certadda.com