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Mastering Hadoop 3

You're reading from   Mastering Hadoop 3 Big data processing at scale to unlock unique business insights

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Product type Paperback
Published in Feb 2019
Publisher Packt
ISBN-13 9781788620444
Length 544 pages
Edition 1st Edition
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Authors (3):
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Timothy Wong Timothy Wong
Author Profile Icon Timothy Wong
Timothy Wong
Manish Kumar Manish Kumar
Author Profile Icon Manish Kumar
Manish Kumar
Chanchal Singh Chanchal Singh
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Chanchal Singh
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Table of Contents (21) Chapters Close

Preface 1. Section 1: Introduction to Hadoop 3 FREE CHAPTER
2. Journey to Hadoop 3 3. Deep Dive into the Hadoop Distributed File System 4. YARN Resource Management in Hadoop 5. Internals of MapReduce 6. Section 2: Hadoop Ecosystem
7. SQL on Hadoop 8. Real-Time Processing Engines 9. Widely Used Hadoop Ecosystem Components 10. Section 3: Hadoop in the Real World
11. Designing Applications in Hadoop 12. Real-Time Stream Processing in Hadoop 13. Machine Learning in Hadoop 14. Hadoop in the Cloud 15. Hadoop Cluster Profiling 16. Section 4: Securing Hadoop
17. Who Can Do What in Hadoop 18. Network and Data Security 19. Monitoring Hadoop 20. Other Books You May Enjoy

Deep dive into the HDFS architecture

As a big data practitioner or enthusiast, you must have read or heard about the HDFS architecture. The goal of this section is to explore the architecture in depth, including the main and essential supporting components. By the end of this section, you will have a deep knowledge of the HDFS architecture, along with the intra-process communication of architecture components. But first, let's start by establishing definition of HDFS (Hadoop Distributed File System). HDFS is the storage system of the Hadoop platform, which is distributed, fault-tolerant, and immutable in nature. HDFS is specifically developed for large datasets (too large to fit in cheaper commodity machines). Since HDFS is designed for large datasets on commodity hardware, it purposely mitigates some of the bottlenecks associated with large datasets.

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