HARTMANN software group

Working with Elastic Search Training in Thousand Oaks

Enroll in or hire us to teach our Working with Elastic Search class in Thousand Oaks, California by calling us @303.377.6176. Like all HSG classes, Working with Elastic Search may be offered either onsite or via instructor led virtual training. Consider looking at our public training schedule to see if it is scheduled: Public Training Classes
Provided there are enough attendees, Working with Elastic Search may be taught at one of our local training facilities.
We offer private customized training for groups of 3 or more attendees.
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Course Description

  

The Elastic Stack is a powerful combination of tools for techniques such as distributed search, analytics, logging, and visualization of data. Elastic Stack 7.0 encompasses new features and capabilities that will enable you to find unique insights into analytics using these techniques. Geared for experienced data analysts, IT professionals, and software developers who seek to augment their data processing and analytics capabilities, Working with Elasticsearch will explore how to use Elastic Stack and Elasticsearch efficiently to build powerful real-time data processing applications.

Throughout the two-day hands-on course, you'll explore the power of this robust toolset that enables advanced distributed search, analytics, logging, and visualization of data, enabled by new features in Elastic Stack 7.0. You'll delve into the core functionalities of Elastic Stack, understanding the role of each component in constructing potent real-time data processing applications. You'll gain proficiency in Elasticsearch for distributed searching and analytics, Logstash for logging, and Kibana for compelling data visualization. You'll also explore the art of crafting custom plugins using Kibana and Beats, and familiarize yourself with Elastic X-Pack, a vital extension for effective security and monitoring.

Course Length: 2 Days
Course Tuition: $1800 (US)

Prerequisites
To ensure a smooth learning experience and maximize the benefits of attending this course, you should have the following prerequisite skills: Knowledge of basic data analysis concepts, including how to work with and interpret data sets, is necessary. Familiarity with basic IT and computer concepts, including operating systems and networks, will facilitate understanding of course content. Since Elastic Stack is used for data processing and analysis, understanding how databases function, and basic SQL skills are beneficial. Some basic programming knowledge, particularly in a language such as Python or Java, will help in understanding and implementing certain concepts, although it's not a hard prerequisite. Experience with command-line interfaces (CLI) would be advantageous, as Elastic Stack often involves interactions via CLI. Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su

Course Outline

  
  1. Introducing Elastic Stack
    • What is Elasticsearch, and why use it?
    • Exploring the components of the Elastic Stack
    • Use cases of Elastic Stack
    • Downloading and installing
  2. Getting Started with Elasticsearch
    • Using the Kibana Console UI
    • Core concepts of Elasticsearch
    • CRUD operations
    • Creating indexes and taking control of mapping
    • REST API overview
  3. Searching - What is Relevant
    • The basics of text analysis
    • Searching from structured data
    • Searching from the full text
    • Writing compound queries
    • Modeling relationships
  4.  Analytics with Elasticsearch
    • The basics of aggregations
    • Preparing data for analysis
    • Metric aggregations
    • Bucket aggregations
    • Pipeline aggregations
    • Substantial Lab and Case Study
  5. Analyzing Log Data
    • Log analysis challenges
    • Using Logstash
    • The Logstash architecture
    • Overview of Logstash plugins
    • Ingest node
  6. Visualizing Data with Kibana
    • Downloading and installing Kibana
    • Preparing data
    • Kibana UI
    • Timelion
    • Using plugins

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