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Getting Started with Elastic Stack 8.0

You're reading from   Getting Started with Elastic Stack 8.0 Run powerful and scalable data platforms to search, observe, and secure your organization

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Product type Paperback
Published in Mar 2022
Publisher Packt
ISBN-13 9781800569492
Length 474 pages
Edition 1st Edition
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Author (1):
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Asjad Athick Asjad Athick
Author Profile Icon Asjad Athick
Asjad Athick
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Toc

Table of Contents (18) Chapters Close

Preface 1. Section 1: Core Components
2. Chapter 1: Introduction to the Elastic Stack FREE CHAPTER 3. Chapter 2: Installing and Running the Elastic Stack 4. Section 2: Working with the Elastic Stack
5. Chapter 3: Indexing and Searching for Data 6. Chapter 4: Leveraging Insights and Managing Data on Elasticsearch 7. Chapter 5: Running Machine Learning Jobs on Elasticsearch 8. Chapter 6: Collecting and Shipping Data with Beats 9. Chapter 7: Using Logstash to Extract, Transform, and Load Data 10. Chapter 8: Interacting with Your Data on Kibana 11. Chapter 9: Managing Data Onboarding with Elastic Agent 12. Section 3: Building Solutions with the Elastic Stack
13. Chapter 10: Building Search Experiences Using the Elastic Stack 14. Chapter 11: Observing Applications and Infrastructure Using the Elastic Stack 15. Chapter 12: Security Threat Detection and Response Using the Elastic Stack 16. Chapter 13: Architecting Workloads on the Elastic Stack 17. Other Books You May Enjoy

Getting insights from data using aggregations

When looking to understand insights in your data, retrieving documents that fit the question you're looking to answer is just the first part of the problem. For example, if an analyst is looking to understand how much traffic their web servers served in a given day, running a query to retrieve logs in the given period may still return millions of events.

Aggregations allow you to summarize large volumes of data into something easier to consume. Elasticsearch can perform two primary types of aggregations:

  • Metric aggregations can calculate metrics such as count, sum, min, max, and average on numeric data.
  • Bucket aggregations can be used to organize large datasets into groups, depending on the value of a field. Buckets can be created based on a range, date, the frequency of a term in the search results (or corpus), and so on.

An exhaustive list of all supported aggregations can be found in the Elasticsearch guide...

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