Book Image

Getting Started with Elastic Stack 8.0

By : Asjad Athick
Book Image

Getting Started with Elastic Stack 8.0

By: Asjad Athick

Overview of this book

The Elastic Stack helps you work with massive volumes of data to power use cases in the search, observability, and security solution areas. This three-part book starts with an introduction to the Elastic Stack with high-level commentary on the solutions the stack can be leveraged for. The second section focuses on each core component, giving you a detailed understanding of the component and the role it plays. You’ll start by working with Elasticsearch to ingest, search, analyze, and store data for your use cases. Next, you’ll look at Logstash, Beats, and Elastic Agent as components that can collect, transform, and load data. Later chapters help you use Kibana as an interface to consume Elastic solutions and interact with data on Elasticsearch. The last section explores the three main use cases offered on top of the Elastic Stack. You’ll start with a full-text search and look at real-world outcomes powered by search capabilities. Furthermore, you’ll learn how the stack can be used to monitor and observe large and complex IT environments. Finally, you’ll understand how to detect, prevent, and respond to security threats across your environment. The book ends by highlighting architecture best practices for successful Elastic Stack deployments. By the end of this book, you’ll be able to implement the Elastic Stack and derive value from it.
Table of Contents (18 chapters)
Section 1: Core Components
Section 2: Working with the Elastic Stack
Section 3: Building Solutions with the Elastic Stack

Architectures to handle complex requirements

Elastic Stack is designed to be extremely flexible in how it can be deployed and used to solve your big data use cases. This section will explore specialized architecture patterns that can be used to handle more complex architectural requirements for your solution.

Elasticsearch (and other stack components) can be configured to be highly available and fault-tolerant in a single geographical region when following standard architecture best practices. Given the sensitive latency and bandwidth requirements for inter-node communication within an Elasticsearch cluster, it is generally not possible to deploy a cluster that spans multiple geographical regions. This presents two main challenges:

  • Standard architecture clusters are not resilient to complete failures within a geographical region.
  • Users and data in other geographies will experience high latencies, pay more in terms of data transfer costs, and have reduced bandwidth for...