TTDS6685: Working with Elasticsearch

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About this Course

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.

Working with Elastic Search is a three-day hands-on course that will provide you with a fundamental understanding of what the stack is all about, and help you use it efficiently to build powerful real-time data processing applications. The first few sections of the course will help you understand how to set up the stack by installing tools, and exploring their basic configurations. You’ll then get up to speed with using Elasticsearch for distributed searching and analytics, Logstash for logging, and Kibana for data visualization. As you work through the course, you will discover the technique of creating custom plugins using Kibana and Beats. This is followed by coverage of the Elastic X-Pack, a useful extension for effective security and monitoring. By the end of this course, you’ll be well versed with the fundamental Elastic Stack functionalities and the role of each component in the stack to solve different data processing problems.

Students who attend this course will learn well versed with the fundamental Elastic Stack functionalities and the role of each component in the stack to solve different data processing problems.

Audience Profile

This course is geared for attendees wants to get guide to storing, managing, and analyzing data with the updated features of Elastic 7.0.

At Course Completion

This skills-focused course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises.. Our engaging instructors and mentors are highly-experienced practitioners who bring years of current, modern "on-the-job" modern AI and machine learning experience into every classroom and hands-on project.

Working in a hands-on lab environment led by our expert instructor, you’ll explore

· New features and updates introduced in Elastic Stack 7.0

· Fundamentals of Elastic Stack including Elasticsearch, Logstash, and Kibana

· Useful tips for using Elastic Cloud and deploying Elastic Stack in production environments

· How to install and configure an Elasticsearch architecture

· How to solve the full-text search problem with Elasticsearch

· Powerful analytics capabilities through aggregations using Elasticsearch

· How to build a data pipeline to transfer data from a variety of sources into Elasticsearch for analysis

· How to create interactive dashboards for effective storytelling with your data using Kibana

· How to secure, monitor and use Elastic Stack’s alerting and reporting capabilities

Working in a hands-on lab environment led by our expert instructor, you’ll explore

· New features and updates introduced in Elastic Stack 7.0

· Fundamentals of Elastic Stack including Elasticsearch, Logstash, and Kibana

· Useful tips for using Elastic Cloud and deploying Elastic Stack in production environments

· How to install and configure an Elasticsearch architecture

· How to solve the full-text search problem with Elasticsearch

· Powerful analytics capabilities through aggregations using Elasticsearch

· How to build a data pipeline to transfer data from a variety of sources into Elasticsearch for analysis

· How to create interactive dashboards for effective storytelling with your data using Kibana

· How to secure, monitor and use Elastic Stack’s alerting and reporting capabilities

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

5. Analyzing Log Data

· Log analysis challenges

· Using Logstash

· The Logstash architecture

· Overview of Logstash plugins

· Ingest node

6. Building Data Pipelines with Logstash

· Parsing and enriching logs using Logstash

· Introducing Beats

· Filebeat

7. Visualizing Data with Kibana

· Downloading and installing Kibana

· Preparing data

· Kibana UI

· Timelion

· Using plugins

8. Elastic X-Pack

· Installing Elasticsearch and Kibana with X-Pack

· Configuring X-Pack

· Securing Elasticsearch and Kibana

· Monitoring Elasticsearch

· Alertin

Prerequisites

Incoming students should possess:

· Basic to Intermediate IT Skills, and Machine Learning knowledge

· Good foundational mathematics or logic skills

· Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su

Attending students should have incoming skills similar to one of the courses below, or should have attended one as a pre-requisite:

· TTML5503 Introduction to AI, AI Programming & Machine Learning | AI/ML JumpStart