Cover image for Learning Kibana 7 - Second Edition

Learning Kibana 7 - Second Edition

Author Bahaaldine Azarmi , Anurag Srivastava
Release Date: 2019/07/01
ISBN: 9781838550363
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Book Description

A beginner's guide to analyzing and visualizing Elasticsearch data using Kibana 7 and Timelion

Key Features

  • Gain a fundamental understanding of how Kibana operates within the Elastic Stack
  • Explore your data with Elastic Graph and create rich dashboards in Kibana
  • Learn scalable data visualization techniques in Kibana 7

Book Description

Kibana is a window into the Elastic Stack that enables the visual exploration and real-time analysis of your data in Elasticsearch. This book will help you understand how you can use Kibana 7 for rich analytics and data visualization.

If you're new to the tool or want to get to grips with the latest features introduced in Kibana 7, this book is the perfect beginner's guide. You'll learn how to set up and configure the Elastic Stack and understand where Kibana sits within the architecture. As you advance, you'll learn how to ingest data from different sources using Beats or Logstash into Elasticsearch, followed by exploring and visualizing data in Kibana. Whether working with time-series data to create complex graphs using Timelion or embedding visualizations created in Kibana into your web applications, this book covers it all. It also covers topics that every Elastic developer needs to be aware of, such as installing and configuring application performance monitoring (APM) servers and agents. Finally, you'll also learn how to create effective machine learning jobs in Kibana to find anomalies in your data.

By the end of this book, you'll have a solid understanding of Kibana, and be able to create your own visual analytics solutions from scratch.

What you will learn

  • Explore the data-driven architecture of the Elastic Stack
  • Install and set up Kibana 7 and other Elastic Stack components
  • Use Beats and Logstash to get input from different data sources
  • Create different visualizations using Kibana
  • Build enterprise-grade Elastic dashboards from scratch
  • Use Timelion to play with time-series data
  • Install and configure APM servers and APM agents
  • Work with Dev Tools, Spaces, Graph, and other important tools

Who this book is for

If you're an aspiring Elastic developer or data analyst, this book is for you. You'll also find it useful if you want to get up to speed with the new features of Kibana 7 and perform data visualizations on enterprise data. No prior knowledge of Kibana is expected, but some experience with Elasticsearch will be helpful.

Downloading the example code for this ebook: You can download the example code files for this ebook on GitHub at the following link: https://github.com/PacktPublishing/Learning-Kibana-7-Second-Edition. If you require support please email: [email protected]

Table of Contents

  1. Title Page
  2. Copyright and Credits
    1. Learning Kibana 7  Second Edition
  3. Dedication
  4. About Packt
    1. Why subscribe?
  5. Contributors
    1. About the authors
    2. About the reviewer
    3. Packt is searching for authors like you
  6. Preface
    1. Who this book is for
    2. What this book covers
    3. To get the most out of this book
      1. Download the example code files
      2. Download the color images
      3. Conventions used
    4. Get in touch
      1. Reviews
  7. Section 1: Understanding Kibana 7
  8. Understanding Your Data for Kibana
    1. Industry challenges
      1. Use cases to explain industry issues
    2. Understanding your data for analysis in Kibana
      1. Data shipping
      2. Data ingestion
      3. Storing data at scale
      4. Visualizing data
    3. Technology limitations
      1. Relational databases
      2. Hadoop
      3. NoSQL
    4. Components of the Elastic Stack
      1. Elasticsearch
      2. Beats
      3. Logstash
      4. Kibana
      5. X-Pack
        1. Security
        2. Monitoring
        3. Alerting
        4. Reporting
    5. Summary
  9. Installing and Setting Up Kibana
    1. Installing Elasticsearch
      1. Elasticsearch installation using the .zip or .tar.gz archives
        1. Downloading and installing using the .zip archive
        2. Downloading and installing using the .tar.gz archive
        3. Running Elasticsearch
      2. Elasticsearch installation on Windows using the .zip package
        1. Downloading and installing the .zip package
        2. Running Elasticsearch
        3. Installing Elasticsearch as a service
      3. Elasticsearch installation using the Debian package
        1. Installing Elasticsearch using the apt repository
        2. Manually installing using the Debian package
      4. Elasticsearch installation using RPM
        1. Installing using the apt repository
        2. Manually installing using RPM
        3. Running Elasticsearch
          1. Running Elasticsearch with SysV
          2. Running Elasticsearch with systemd
        4. Checking whether Elasticsearch is running
    2. Installing Kibana
      1. Kibana installation using the .zip or .tar.gz archives
        1. Downloading and installing using the .tar.gz archive
          1. Running Kibana
        2. Downloading and installing using the .zip archive
          1. Running Kibana
      2. Kibana installation using the Debian package
        1. Installing using the apt repository
        2. Manually installing Kibana using the Debian package
        3. Running Kibana
          1. Running Kibana with SysV
          2. Running Kibana with systemd
      3. Kibana installation using RPM
        1. Installing using the apt repository
        2. Manually installing using RPM
        3. Running Kibana
          1. Running Kibana with SysV
          2. Running Kibana with systemd
    3. Installing Logstash
      1. Installing Logstash using the downloaded binary
      2. Installing Logstash from the package repositories
        1. Installing Logstash using the apt package
        2. Installing Logstash using the yum package
      3. Running Logstash as a service
        1. Running Logstash using systemd
        2. Running Logstash using upstart
        3. Running Logstash using SysV
    4. Installing Beats
      1. Installing Filebeat
        1. deb
        2. rpm
        3. macOS
        4. Linux
        5. win
      2. Installing Metricbeat
        1. deb
        2. rpm
        3. macOS
        4. Linux
        5. win
      3. Installing Packetbeat
        1. deb
        2. rpm
        3. macOS
        4. Linux
        5. win
      4. Installing Heartbeat
        1. deb
        2. rpm
        3. macOS
        4. Linux
        5. win
      5. Installing Winlogbeat
    5. Summary
  10. Section 2: Exploring the Data
  11. Business Analytics with Kibana
    1. Understanding logs
    2. Data modeling
    3. Importing data
      1. Beats
        1. Configuring Filebeat to import data we need to enable the following command in the input section of the filebeat.yml file
          1. Reading log files using Filebeat
      2. Logstash
        1. Reading CSV data using Logstash
        2. Reading MongoDB data using Logstash
        3. Reading MySQL data using Logstash
    4. Creating an index pattern
    5. Summary
  12. Visualizing Data Using Kibana
    1. Creating visualizations in Kibana
      1. Identifying the data to visualize
      2. Creating an area chart, a line chart, and a bar chart
      3. Creating a pie chart
      4. Creating the heatmap
      5. Creating the data table
      6. Creating the metric visualization
      7. Creating the tag cloud
      8. Inspecting the visualization
      9. Sharing the visualization
    2. Creating dashboards in Kibana
      1. Sharing the dashboard
      2. Generating reports
    3. Summary
  13. Section 3: Tools for Playing with Your Data
  14. Dev Tools and Timelion
    1. Introducing Dev Tools
      1. Console
      2. Search profiler
        1. Aggregation profile
      3. Grok Debugger
    2. Timelion
      1. .es()
      2. .label()
      3. .color()
      4. .static()
      5. .bars()
      6. .points()
      7. .derivative()
      8. .holt()
      9. .trend()
      10. .mvavg()
      11. A use case of Timelion
    3. Summary
  15. Space and Graph Exploration in Kibana
    1. Kibana spaces
      1. Creating a space
      2. Editing a space
      3. Deleting a space
      4. Switching between spaces
      5. Moving saved objects between spaces
      6. Restricting space access
        1. Creating a role to provide access to a space
        2. Creating a user and assigning the space access role
        3. Checking the user space access
    2. Kibana graphs
      1. Differences with industry graph databases
      2. Creating a Kibana graph
      3. Advanced graph exploration
    3. Summary
  16. Section 4: Advanced Kibana Options
  17. Elastic Stack Features
    1. Security
      1. Roles
      2. Users
    2. Monitoring
      1. Elasticsearch Monitoring
      2. Kibana Monitoring
    3. Alerting
      1. Creating a threshold alert
    4. Reporting
      1. CSV reports
      2. PDF and PNG reports
    5. Summary
  18. Kibana Canvas and Plugins
    1. Kibana Canvas
      1. Introduction to Canvas
        1. Customizing the workpad
        2. Managing assets
      2. Adding elements
        1. Data tables
          1. Designing the data table
      3. Pie charts
      4. Images
      5. Creating a presentation in Canvas
    2. Kibana plugins
      1. Installing plugins
      2. Removing plugins
      3. Available plugins
    3. Summary
  19. Application Performance Monitoring
    1. APM components
      1. APM agents
      2. The APM Server
        1. Installing the APM Server
          1. APT
          2. YUM
          3. APM Server installation on Windows
        2. Running the APM Server
        3. Configuring the APM Server
      3. Elasticsearch
      4. Kibana
    2. Configuring an application with APM
      1. Configuring the APM agent for the Django application
      2. Running the Django application
      3. Monitoring the APM data
    3. Summary
  20. Machine Learning with Kibana
    1. What is Elastic machine learning?
      1. Machine learning features
      2. Creating machine learning jobs
        1. Data visualizer
        2. Single metric jobs
          1. Practical use case to explain machine learning
          2. Forecasting using machine learning
        3. Multi-metric jobs
        4. Population jobs
        5. Job management
          1. Job settings
          2. Job config
          3. Datafeed
          4. Counts
          5. JSON
          6. Job messages
          7. Datafeed preview
          8. Forecasts
    2. Summary
  21. Other Books You May Enjoy
    1. Leave a review - let other readers know what you think
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