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Mastering Elastic Stack

You're reading from   Mastering Elastic Stack Dive into data analysis with a pursuit of mastering ELK Stack on real-world scenarios.

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Product type Paperback
Published in Feb 2017
Publisher Packt
ISBN-13 9781786460011
Length 526 pages
Edition 1st Edition
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Authors (2):
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 Kumar Gupta Kumar Gupta
Author Profile Icon Kumar Gupta
Kumar Gupta
 Gupta Gupta
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Gupta
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Table of Contents (19) Chapters Close

Mastering Elastic Stack
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
1. Elastic Stack Overview FREE CHAPTER 2. Stepping into Elasticsearch 3. Exploring Logstash and Its Plugins 4. Kibana Interface 5. Using Beats 6. Elastic Stack in Action 7. Customizing Elastic Stack 8. Elasticsearch APIs 9. X-Pack: Security and Monitoring 10. X-Pack: Alerting, Graph, and Reporting 11. Best Practices 12. Case Study-Meetup

How Beats differ from Logstash


From what we have understood about Beats, it looks fairly similar to Logstash, wherein we have input plugins that are used to parse the data. So, how does Beats differ? Beats are data shippers shipping data from a variety of inputs such as files, data streams, or logs whereas Logstash is a data parser. Though Logstash can ship data, it's not its primary usage. In a nutshell, Beats and Logstash are similar in functionality, but there are glaring differences between them both in terms of how they are developed and the underlying technology used.

Let's look at the differences between them:

  • Logstash consumes a lot of memory and requires a higher amount of resources, whereas Beats requires fewer resources and consumes low memory.

  • Logstash is heavy to install on all the systems from which you want to collect the logs, whereas Beats are lightweight data shippers that will ship your data from multiple systems.

  • If you are collecting logs from multiple systems, then you...

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