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Elastic Stack - Storing and Analyzing Logs

34.000 CZK

Price (without VAT)

Days2
2. 5. 3. 5. 2024
virtual
CZ
20. 6.21. 6. 2024
virtual
CZ

The course is designed for anyone who wants to learn how to store large amounts of data using Elastic Stack. We learn how to store, search, and visualize logs using Kibana. Gradually we will go through the whole process from installing individual components (Beats, Logstash, Elasticsearch, Kibana) through their use to cluster management.

On real-life examples, we try different storage architectures that we collect from different sources, enriching them with additional information and storing them into Elasticsearch. The participant gets acquainted with the Elasticsearch repository so that it can efficiently manage and scalable a large amount of data. In Kibana, we learn how to visualize logs, create dashboards, and understand the data more deeply.

Audience

  • Application developers
  • System Administrators
  • IT Professionals

Goals

Participants will learn:

  • how to store different logs using Elastic Stack
  • how to design logging architecture for different uses
  • how to install and configure individual data processing components (Beats, Logstash, Elasticsearch, Kibana)
  • Elasticsearch technology more in depth, learn how to use storage tools, how to manage, scale and monitor
  • create dashboards and work with the Kibana tool

Course gaurantor

PETR NOVOTNÝ

Petr's knowledge goes from solution architecture to development (JavaScript, PHP) through Elasticsearch, Oracle, PL/SQL to agile methodology and SCRUM. At the same time, Petr has been working with Elasticsearch technology for several years and has become one of our main instructors.
 

Outline

Introduction to Elastic Stack

  • Understanding the significance of log, metrics, traces, and availability data collection
  • Introduction to individual components of Observability in Elastic Stack

Elasticsearch as a log storage solution

  • Introduction to Elasticsearch, basic operations with Kibana
  • Design and sizing of a log collection cluster
  • Cluster configuration
  • Various deployment architectures for Elastic Stack
  • Indexes and their design, data streams
  • Distributed Elasticsearch model
  • In-depth explanation of Elasticsearch principles, Apache Lucene, and more
  • Types of individual nodes

Mapping

  • Document mapping in Elasticsearch for logs
  • Dynamic fields
  • Runtime fields
  • ECS 

Searching in Elasticsearch

  • Various search options in Elasticsearch and how to navigate them Lucene
  • KQL 
  • EQL 
  • SQL
  • ES|QL
  • Examples of searching on learning datasets

Log collection

  • Log collection using Filebeat
  • Basic log processing
  • Collection using Elastic Agent
  • Fleet server and its configuration
  • Kibana Fleet

Log processing

  • Log processing using Ingest node
  • Basic processors for log processing
  • Dissect filter, Grok filter, Attachment, and more

Logstash

  • Integration of Logstash into log processing architecture
  • Pipelines, inputs, filters, outputs
  • Data collection from various sources
  • Logstash queues
  • Monitoring Logstash-to-Logstash and various usage architectures

APM 

  • APM server
  • APM agents
  • Sample application and its integration with Elastic
  • APM Errors, Performance Metrics
  • SLI, SLO, SLA using APM
  • Distributed tracing
  • Microservice architecture

Kibana

  • Configuration, data views
  • Discover interface
  • Aggregation using Kibana
  • Kibana Lens
  • Creation of visualizations
  • Dashboards
  • Searching in data
  • Sample dashboards, real-world examples

Data and index management

  • Capacity planning and configuration
  • Index design for logging, data streams
  • Rollover, Shrink, Merge ILM and rollover of indexes over time
  • Searchable snapshots
  • Transforms

Cluster management

  • Restart (rolling, full-cluster)
  • Snapshot and repository management
  • Cluster upgrade (minor, major versions)
  • Lab

Cluster monitoring

  • Setting up Elastic cluster monitoring
  • What, when, and how to monitor
  • Monitoring tools
  • Lab

Technical requirements

  • Computer with any OS (Linux, Windows, OS X)
  • SSH client (eg Windows Winsshterm, Putty), permission to connect remotely to SSH (port 22)
  • Web browser

Prerequisites

Basic knowledge of Elasticsearch, HTTP protocol, JSON format, general knowledge of database systems.


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