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

Working with geospatial datasets using Maps

Elasticsearch comes with great support for geospatial data out of the box. Geo-point fields can hold a single geographic location (latitude/longitude pair) while Geo-shape fields support the encoding of arbitrary geoshapes (such as lines, squares, polygons, and so on). When searching for data on Elasticsearch, users can also leverage a range of geo queries, such as geo_distance (which finds docs containing a geo-point within a given distance from a specified geo_point) and geo_bounding_box (which finds docs with geo-points falling inside a specified geographical boundary). Kibana Maps is the visual interface for the geospatial capabilities on Elasticsearch.

Geospatial data is useful (and rather common) in several use cases. For example, logs containing public addresses will often contain (or can be enriched with) geo-location information for the corresponding host.

Analysts can use this context to understand whether connections to certain...