Elastic Stack 8. x Cookbook : Over 80 Recipes to Perform Ingestion, Search, Visualization, and Monitoring for Actionable Insights / Huage Chen, Yazid Akadiri, and Shay Banon.

Format:

Book

Author/Creator:

Chen, Huage, author.

Akadiri, Yazid, author.

Banon, Shay, author.

Language:

English

Subjects (All):

Client/server computing.

Open source software.

Physical Description:

1 online resource (688 pages)

Edition:

First edition.

Place of Publication:

Birmingham, England : Packt Publishing, [2024]

Summary:

Unlock the full potential of Elastic Stack for search, analytics, security, and observability and manage substantial data workloads in both on-premise and cloud environments Key Features Explore the diverse capabilities of the Elastic Stack through a comprehensive set of recipes Build search applications, analyze your data, and observe cloud-native applications Harness powerful machine learning and AI features to create data science and search applications Purchase of the print or Kindle book includes a free PDF eBook Book Description Learn how to make the most of the Elastic Stack (ELK Stack) products--including Elasticsearch, Kibana, Elastic Agent, and Logstash--to take data reliably and securely from any source, in any format, and then search, analyze, and visualize it in real-time. This cookbook takes a practical approach to unlocking the full potential of Elastic Stack through detailed recipes step by step. Starting with installing and ingesting data using Elastic Agent and Beats, this book guides you through data transformation and enrichment with various Elastic components and explores the latest advancements in search applications, including semantic search and Generative AI. You'll then visualize and explore your data and create dashboards using Kibana. As you progress, you'll advance your skills with machine learning for data science, get to grips with natural language processing, and discover the power of vector search. The book covers Elastic Observability use cases for log, infrastructure, and synthetics monitoring, along with essential strategies for securing the Elastic Stack. Finally, you'll gain expertise in Elastic Stack operations to effectively monitor and manage your system. What you will learn Discover techniques for collecting data from diverse sources Visualize data and create dashboards using Kibana to extract business insights Explore machine learning, vector search, and AI capabilities of Elastic Stack Handle data transformation and data formatting Build search solutions from the ingested data Leverage data science tools for in-depth data exploration Monitor and manage your system with Elastic Stack Who this book is for This book is for Elastic Stack users, developers, observability practitioners, and data professionals ranging from beginner to expert level. If you're a developer, you'll benefit from the easy-to-follow recipes for using APIs and features to build powerful applications, and if you're an observability practitioner, this book will help you with use cases covering APM, Kubernetes, and cloud monitoring. For data engineers and AI enthusiasts, the book covers dedicated recipes on vector search and machine learning. No prior knowledge of the Elastic Stack is required.

Contents:

Cover

Title Page

Copyright and Credits

Dedication

Foreword

Contributors

Acknowledgments

Table of Contents

Preface

Chapter 1: Getting Started - Installing the Elastic Stack

Deploying the Elastic Stack on Elastic Cloud

How to do it…

How it works…

There's more…

Installing the Elastic Stack with ECK

Technical requirements

Getting ready

See also

Installing a self-managed Elastic Stack

Creating and setting up data tiering

How to do it on your local machine…

How it works (on self-managed)…

How to do it on Elastic Cloud…

How to do it on ECK…

Creating and setting up additional Elasticsearch nodes

How to do it...

How to do it on Elastic Cloud...

Creating and setting up Fleet Server

How to do it on a self-managed Elastic Stack…

Setting up on Elastic Cloud

Setting up snapshot repository

Chapter 2: Ingesting General Content Data

Introducing the Wikipedia Movie Plots dataset

Adding data from the Elasticsearch client

How it works...

Updating data in Elasticsearch

Deleting data in Elasticsearch

Using an analyzer

Defining index mapping

How it works.

There's more…

Using dynamic templates in document mapping

Creating an index template

Indexing multiple documents using Bulk API

Chapter 3: Building Search Applications

Searching with Query DSL

There's more...

Building advanced search queries with Query DSL

Using search templates to pre-render search requests

Getting started with Search Applications for your Elasticsearch index

Building a search experience with the Search Application client

Measuring the performance of your Search Applications with Behavioral Analytics

Chapter 4: Timestamped Data Ingestion

Deploying Elastic Agent with Fleet

Monitoring Apache HTTP logs and metrics using the Apache integration

Deploying standalone Elastic Agent

Adding data using Beats

See also.

Setting up a data stream manually

Dataset

Setting up a time series data stream manually

Chapter 5: Transform Data

Creating an ingest pipeline

Enriching data with a custom ingest pipeline for an existing Elastic Agent integration

Using a processor to enrich your data before ingesting with Elastic Agent

Installing self-managed Logstash

Creating a Logstash pipeline

Setting up pivot data transform

Setting up the latest data transform

Downsampling your time series data

Chapter 6: Visualize and Explore Data

Exploring your data in Discover

Exploring your data with ES|QL

Creating visualizations with Kibana Lens

Creating visualizations from runtime fields

Getting ready.

How to do it...

Creating Kibana maps

Creating and using Kibana dashboards

Creating Canvas workpads

Chapter 7: Alerting and Anomaly Detection

Creating alerts in Kibana

Monitoring alert rules

Investigating data with log rate analysis

Investigating data with log pattern analysis

Investigating data with change point detection

Detecting anomalies in your data with unsupervised machine learning jobs

Creating anomaly detection jobs from a Lens visualization

Chapter 8: Advanced Data Analysis and Processing

Finding deviations in your data with outlier detection

Building a model to perform regression analysis

Building a model for classification

Using a trained model for inference.

Getting ready

Deploying third-party NLP models and testing via the UI

Running advanced data processing with trained models

Chapter 9: Vector Search and Generative AI Integration

Implementing semantic search with dense vectors

Implementing semantic search with sparse vectors

Using hybrid search to build advanced search applications

Developing question-answering applications with Generative AI

Using advanced techniques for RAG applications

Chapter 10: Elastic Observability Solution

Instrumenting your application with Elastic APM

Setting up RUM

Instrumenting and monitoring with OpenTelemetry

Monitoring Kubernetes environments with Elastic Agent

Managing synthetics monitoring

Gaining comprehensive system visibility with Elastic Universal Profiling.

Notes:

Description based on publisher supplied metadata and other sources.

Description based on print version record.

ISBN:

9781837633500

1837633509

OCLC:

1439567408