Getting Started with Elastic Stack 8.0: Run powerful and scalable data platforms to search, observe, and secure your organization

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Use the Elastic Stack for search, security, and observability-related use cases while working with large amounts of data on-premise and on the cloudKey FeaturesLearn the core components of the Elastic Stack and how they work togetherBuild search experiences, monitor and observe your environments, and defend your organization from cyber attacksGet to grips with common architecture patterns and best practices for successfully deploying the Elastic StackBook DescriptionThe 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.What you will learnConfigure Elasticsearch clusters with different node types for various architecture patternsIngest different data sources into Elasticsearch using Logstash, Beats, and Elastic AgentBuild use cases on Kibana including data visualizations, dashboards, machine learning jobs, and alertsDesign powerful search experiences on top of your data using the Elastic StackSecure your organization and learn how the Elastic SIEM and Endpoint Security capabilities can helpExplore common architectural considerations for accommodating more complex requirementsWho this book is forDevelopers and solutions architects looking to get hands-on experience with search, security, and observability-related use cases on the Elastic Stack will find this book useful. This book will also help tech leads and product owners looking to understand the value and outcomes they can derive for their organizations using Elastic technology. No prior knowledge of the Elastic Stack is required.Table of ContentsIntroduction to the Elastic StackInstalling and Running the Elastic StackIndexing and Searching for DataLeveraging Insights and Managing Data on ElasticsearchRunning Machine Learning Jobs on ElasticsearchCollecting and Shipping Data with BeatsUsing Logstash to Extract, Transform, and Load DataInteracting with Your Data on KibanaManaging Data Onboarding with Elastic AgentBuilding Search Experiences Using the Elastic StackObserving Applications and Infrastructure Using the Elastic StackSecurity Threat Detection and Response Using the Elastic StackArchitecting Workloads on the Elastic Stack

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Questions about Book


pouriya
pouriya

imagin you want to create a search system using elastic
what factor do you thing about it ?

Refhub AI
Book's Answer:
( AI as Asjad Athick )

When creating a search system using Elastic Stack, there are several factors to consider. As outlined in my book "Getting Started with Elastic Stack 8.0", here are some key factors to keep in mind:

1. Data Sources: Identify the data sources that need to be indexed and searched. Determine the type of data, its structure, and the frequency of updates.

2. Data Model: Decide on the data model to use, such as a relational database, XML, or JSON. The data model should be flexible to accommodate changing requirements.

3. Indexing: Determine if the data needs to be processed and transformed before indexing. This may involve data cleaning, filtering, or parsing.

4. Querying: Decide on the query types and their complexity, such as exact matches, fuzzy matches, or filtering by specific fields.

5. Scalability: Consider the expected volume of queries and data, and choose an appropriate hardware configuration or distributed setup.

6. Security: Determine security requirements, such as authentication, authorization, and data encryption, to ensure the search system is secure.

7. Performance: Optimize the search system for performance, considering factors like query latency, response size, and data retrieval speed.

8. Maintenance: Plan for regular indexing, log collection, and software updates to ensure the search system remains efficient and effective.

By carefully considering these factors, you can design an Elastic Stack-based search system that meets your specific needs.


Reviews:


4.0

Based on 1 review

merkousha
merkousha

Feb. 4, 2024, 7:52 a.m.

it's an amazing stack i work with it in 3 companies and result is same : Amazing!



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