Chapter 1, Introducing Elastic Stack, motivates you by introducing the core components of Elastic Stack, and the importance of the distributed, scalable search and analytics that Elastic Stack offers by means of use cases involving Elasticsearch. The chapter provides a brief introduction to all the core components, where they fit into the overall stack, and the purpose of each component. It concludes with instructions for downloading and installing Elasticsearch and Kibana to get started.
Chapter 2, Getting Started with Elasticsearch, introduces the core concepts involved in Elasticsearch, which form the backbone of the Elastic Stack. Concepts such as indexes, types, nodes, and clusters are introduced. You will also be introduced to the REST API to perform essential operations, datatypes, and mappings.
Chapter 3, Searching – What is Relevant, focuses on the search use case of Elasticsearch. It introduces the concepts of text analysis, tokenizers, analyzers, and the need for analysis and relevance-based searches. The chapter highlights an example use case to cover the relevance-based search topics.
Chapter 4, Analytics with Elasticsearch, covers various types of aggregations by means of examples in order for you to acquire an in-depth understanding. This chapter covers very simple to complex aggregations to get powerful insights from terabytes of data. The chapter also covers the motivation behind using different types of aggregations.
Chapter 5, Analyzing Log Data, establishes the foundation for the motivation behind Logstash, its architecture, and installing and configuring Logstash to set up basic data pipelines. Elastic 5 introduced ingest nodes, which can be used instead of a dedicated Logstash setup. This chapter also covers building pipelines using Elastic ingest nodes.
Chapter 6, Building Data Pipelines with Logstash, builds on the fundamental knowledge of Logstash by means of transformations and aggregation-related filters. It covers how the rich set of filters brings Logstash closer to the other real-time and near real-time stream processing frameworks with zero coding. It introduces the Beats platform, along with FileBeat components, to transport log files from edge machines.
Chapter 7, Visualizing Data with Kibana, covers how to effectively use Kibana to build beautiful dashboards for effective story telling regarding your data. It uses a sample dataset and provides step-by-step guidance on creating visualizations with just a few clicks.
Chapter 8, Elastic X-Pack, covers how to add the extensions required for specific use cases. Elastic X-Pack is a set of extensions developed and maintained by Elastic Stack developers. These extensions are maintained with consistent versioning.
Chapter 9, Running Elastic Stack in Production, puts together a complete application for sensor data analytics with the concepts learned so far. It is entirely reliant on Elastic Stack components and close to zero programming. It shows how to model your data in Elasticsearch, how to build the data pipeline to ingest data, and then visualize it using Kibana. It also demonstrates how to effectively use X-Pack components to secure, monitor, and get alerts when certain conditions are met in this real-world example.
Chapter 10, Building a Sensor Data Analytics Application, covers recommendations on how to deploy Elastic Stack to production. ElasticSearch can be deployed to solve a variety of use cases, such as product search, log analytics, and sensor data analytics. This chapter provides recommendations for taking your application to production. It provides guidelines on typical configurations that need to be looked at for different use cases. It also covers deployment in cloud-based hosted providers such as Elastic Cloud.
Chapter 11, Monitoring Server Infrastructure, shows how you can use Elastic Stack to set up a real-time monitoring solution for your servers and applications that is built entirely using Elastic Stack. This can help prevent and minimize downtime while also improving the end user experience.
