Johns Hopkins University

YARN MapReduce Architecture and Advanced Programming

Johns Hopkins University

YARN MapReduce Architecture and Advanced Programming

This course is part of Big Data Processing Using Hadoop Specialization

Karthik Shyamsunder

Instructor: Karthik Shyamsunder

Access provided by Samsung Research, Bangalore

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
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.

Details to know

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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

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

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

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

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

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Instructor

Karthik Shyamsunder
Johns Hopkins University
4 Courses 1,276 learners

Offered by

Johns Hopkins University

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