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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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Toc

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

Micro-batch processing case study

This section covers a small case study that is used to detect an IP default with Kafka and Spark Streaming, and the IP has attempted to hit the server many times. We will cover the following use cases:

  • Producer: The Kafka producer API will be used to read a log file and publish documents on the topic of Kafka. In a real case, however, we could use the flume or producer application, which records in real time directly and publishes on Kafka.
  • Fraud IPs list: We will keep a list of predefined IP frauds to identify the IPs for fraud. We use an in-memory IP list for this application, which can be substituted by fast key-based searching, such as HBase.
  • Spark Streaming: Spark Streaming applications can read Kafka records and detect suspicious IPs and domains.

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