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Mastering Elastic Stack

Mastering Elastic Stack

By : Kumar Gupta, Gupta
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Mastering Elastic Stack

Mastering Elastic Stack

1 (1)
By: Kumar Gupta, Gupta

Overview of this book

Even structured data is useless if it can’t help you to take strategic decisions and improve existing system. If you love to play with data, or your job requires you to process custom log formats, design a scalable analysis system, and manage logs to do real-time data analysis, this book is your one-stop solution. By combining the massively popular Elasticsearch, Logstash, Beats, and Kibana, elastic.co has advanced the end-to-end stack that delivers actionable insights in real time from almost any type of structured or unstructured data source. If your job requires you to process custom log formats, design a scalable analysis system, explore a variety of data, and manage logs, this book is your one-stop solution. You will learn how to create real-time dashboards and how to manage the life cycle of logs in detail through real-life scenarios. This book brushes up your basic knowledge on implementing the Elastic Stack and then dives deeper into complex and advanced implementations of the Elastic Stack. We’ll help you to solve data analytics challenges using the Elastic Stack and provide practical steps on centralized logging and real-time analytics with the Elastic Stack in production. You will get to grip with advanced techniques for log analysis and visualization. Newly announced features such as Beats and X-Pack are also covered in detail with examples. Toward the end, you will see how to use the Elastic stack for real-world case studies and we’ll show you some best practices and troubleshooting techniques for the Elastic Stack.
Table of Contents (13 chapters)
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Using a message broker


The traffic on production servers is supposed to be high at times. When this happens, log entries and statistics data become very critical and the amount of such data in total is also high. All of the Beats will be doing their work and will be sending respective data to Elasticsearch, but it is possible that some of the packets/data are lost while processing. It may happen because of a network failure, very high peaks of data, or any other possible reason. The point is, the data being indexed must not be lost in any case.

To address this problem, using a message broker or buffer is a good choice. There are many tools that can be evaluated for your choice of the message broker. For Open Source, there are two good tools available:

Sometimes, it seems that message broker is a must for a production environment, but there might be cases when we don't need any message broker at all. When we use Filebeat, it acts as...

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