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.
YARN MapReduce Architecture and Advanced Programming
YARN MapReduce Architecture and Advanced Programming
本课程是 Big Data Processing Using Hadoop 专项课程 的一部分
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您将学到什么
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.
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作业
12 项作业
授课语言：英语（English）
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本课程是 Big Data Processing Using Hadoop 专项课程 专项课程的一部分
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该课程共有5个模块
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.
涵盖的内容
2篇阅读材料
In this module, we will cover the architecture YARN architecture and architectural capabilities followed by MapReduce architecture built on YARN
涵盖的内容
6个视频4篇阅读材料3个作业
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.
涵盖的内容
6个视频5篇阅读材料3个作业
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.
涵盖的内容
6个视频5篇阅读材料3个作业
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.
涵盖的内容
7个视频5篇阅读材料3个作业
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