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Modern Big Data Processing with Hadoop

You're reading from   Modern Big Data Processing with Hadoop Expert techniques for architecting end-to-end big data solutions to get valuable insights

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Product type Paperback
Published in Mar 2018
Publisher Packt
ISBN-13 9781787122765
Length 394 pages
Edition 1st Edition
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Concepts
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Authors (3):
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Manoj R Patil Manoj R Patil
Author Profile Icon Manoj R Patil
Manoj R Patil
Prashant Shindgikar Prashant Shindgikar
Author Profile Icon Prashant Shindgikar
Prashant Shindgikar
V Naresh Kumar V Naresh Kumar
Author Profile Icon V Naresh Kumar
V Naresh Kumar
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Toc

Table of Contents (12) Chapters Close

Preface 1. Enterprise Data Architecture Principles FREE CHAPTER 2. Hadoop Life Cycle Management 3. Hadoop Design Consideration 4. Data Movement Techniques 5. Data Modeling in Hadoop 6. Designing Real-Time Streaming Data Pipelines 7. Large-Scale Data Processing Frameworks 8. Building Enterprise Search Platform 9. Designing Data Visualization Solutions 10. Developing Applications Using the Cloud 11. Production Hadoop Cluster Deployment

Use case

Let's assume that we have an application deployed on an application server. That application is logging on to an access log. Then how can we analyze this access log using a dashboard? We would like to create a real-time visualization of the following info:

  • Number of various response codes
  • Total number of responses
  • List of IPs

Proposed technology stack:

  • Filebeat: To read access log and write to Kafka topic
  • Kafka: Message queues and o buffer message
  • Logstash: To pull messages from Kafka and write to Elasticsearch index
  • Elasticsearch: For indexing messages
  • Kibana: Dashboard visualization

In order to solve this problem, we install filebeat on Appserver. Filebeat will read each line from the access log and write to the kafka topic in real time. Messages will be buffered in Kafka. Logstash will pull messages from the Kafka topic and write to Elasticsearch.

Kibana will...

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