YARN MapReduce Architecture and Advanced Programming

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YARN MapReduce Architecture and Advanced Programming

This course is part of Big Data Processing Using Hadoop Specialization

Instructor: Karthik Shyamsunder

Included with

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5 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

5 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Learn the fundamentals of YARN and MapReduce architectures, including how they work together to process large-scale data efficiently.
Understand and implement Mapper and Reducer parallelism in MapReduce jobs to improve data processing efficiency and scalability.
Apply optimization techniques such as combiners, partitioners, and compression to enhance the performance and I/O operations of MapReduce jobs.
Explore advanced concepts like multithreading, speculative execution, input/output formats, and how to avoid common MapReduce anti-patterns.

Skills you'll gain

Tools you'll learn

Apache Hadoop

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

12 assignments

Taught in English

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This course is part of the Big Data Processing Using Hadoop Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

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There are 5 modules in this course

The course "YARN MapReduce Architecture and Advanced Programming" provides an in-depth understanding of YARN and MapReduce architectures, focusing on their components and capabilities. Students will explore the MapReduce programming model and learn essential optimization techniques such as combiners, partitioners, and compression to improve job performance. The course covers Mapper and Reducer parallelism in MapReduce, along with practical steps for writing and configuring MapReduce jobs. Advanced topics such as multithreading, speculative execution, and input/output formats are also explored.

By the end of the course, participants will have hands-on experience in optimizing and writing efficient MapReduce jobs, preparing them to apply best practices in real-world scenarios. This course is unique as it not only covers the foundational aspects of YARN and MapReduce but also delves into optimization strategies, offering learners the tools to enhance data processing efficiency. Whether you're new to MapReduce or looking to deepen your knowledge, this course provides valuable insights for mastering large-scale data processing.

This course provides a comprehensive introduction to YARN and MapReduce architectures, covering their fundamental components and capabilities. You will explore the MapReduce programming model, focusing on optimization techniques such as combiners, partitioners, and compression. Key concepts like Mapper and Reducer parallelism will be demonstrated, alongside practical steps for writing and configuring MapReduce jobs. The course also delves into advanced topics such as multithreading, speculative execution, and input/output formats. By the end, You will gain a deep understanding of MapReduce and be equipped to apply best practices in real-world scenarios.

What's included

2 readings

In this module, we will cover the architecture YARN architecture and architectural capabilities followed by MapReduce architecture built on YARN

What's included

6 videos4 readings3 assignments

6 videos Total 105 minutes

Introduction - YARN and MapReduce Architecture 2 minutes
YARN Architecture 36 minutes
YARN Architectural Capabilities 14 minutes
MapReduce Framework on YARN 27 minutes
MapReduce Programming Paradigm 24 minutes
Summary - YARN and MapReduce Architecture 2 minutes

4 readings Total 70 minutes

YARN Architecture and its Capabilities 10 minutes
MapReduce Framework on YARN 10 minutes
MapReduce Programming Paradigm 10 minutes
Self-Reflective Reading: Unlocking Big Compute with YARN 40 minutes

3 assignments Total 90 minutes

YARN and MapReduce Architecture 60 minutes
Introduction - YARN and MapReduce Architecture 15 minutes
YARN and MapReduce: Framework, Programming Paradigm, and Summary 15 minutes

This module provides a comprehensive overview of the MapReduce API, guiding you through the steps to write a MapReduce program. It covers the concepts of Mapper and Reducer parallelism, illustrating their implementation and impact on data processing efficiency.

What's included

6 videos5 readings3 assignments

6 videos Total 124 minutes

Introduction - MapReduce Programming Basics 2 minutes
MapReduce Programming API 31 minutes
Steps to Write a MapReduce Program 36 minutes
Mapper Parallelism 25 minutes
Reducer Parallelism 28 minutes
Summary - MapReduce Programming Basics 2 minutes

5 readings Total 80 minutes

MapReduce Programming API 10 minutes
Steps to Write a MapReduce Program 10 minutes
Mapper Parallelism 10 minutes
Reducer Parallelism 10 minutes
Self-Reflective Reading: Mapping Data Processing in Your Industry 40 minutes

3 assignments Total 90 minutes

MapReduce Programming Basics 60 minutes
Introduction to MapReduce: Programming Basics, API, and Development Steps 15 minutes
MapReduce Programming Basics: Mapper and Reducer Parallelism Overview 15 minutes

This module focuses on advanced MapReduce optimization techniques, including the use of combiners to enhance performance, partitioners to manage data distribution across reducers, and compression methods to optimize I/O. It also covers the application of counters to collect and analyze statistics about MapReduce jobs.

What's included

6 videos5 readings3 assignments

6 videos Total 87 minutes

Introduction - MapReduce Programming Intermediate 2 minutes
Combiners 29 minutes
Partitioners 17 minutes
Compression 18 minutes
Counters 18 minutes
Summary - MapReduce Programming Intermediate 3 minutes

5 readings Total 75 minutes

Combiners 10 minutes
Partitioners 5 minutes
Compression 10 minutes
Counters 10 minutes
Self-Reflective Reading: Balancing Storage and Processing in Data Management 40 minutes

3 assignments Total 90 minutes

MapReduce Programming Intermediate 60 minutes
Intermediate MapReduce Programming: Combiners and Partitioners 15 minutes
Intermediate MapReduce Programming: Compression, Counters, and Summary 15 minutes

This module explores advanced MapReduce concepts including multithreading, the internals of input/output formats, and speculative execution. It also covers running jobs locally and identifies common MapReduce anti-patterns to avoid.

What's included

7 videos5 readings3 assignments

7 videos Total 71 minutes

Introduction - MapReduce Advanced 1 minute
Multithreading 16 minutes
Input-Output Format Internals 21 minutes
Speculative Execution 7 minutes
Running Jobs Locally 8 minutes
MapReduce Anti-Patterns 15 minutes
Summary - MapReduce Advanced 2 minutes

5 readings Total 60 minutes

Multithreading 5 minutes
Input-Output Format Internals 5 minutes
Running Jobs Locally 5 minutes
MapReduce Anti-Patterns 5 minutes
Self-Reflective Reading: Best Practices for Effective MapReduce Development and Testing 40 minutes

3 assignments Total 90 minutes

MapReduce Programming Advanced 60 minutes
Advanced MapReduce: Multithreading, I/O Formats, and Speculative Execution 15 minutes
Advanced MapReduce: Local Job Execution, Anti-Patterns, and Summary 15 minutes

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Instructor

Karthik Shyamsunder

Johns Hopkins University

4 Courses 1,277 learners

Offered by

Johns Hopkins University

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