Learning Path: The Road To Elasticsearch

Learn all you need to know about Elasticsearch and get started with the new Elastic Stack.

Ever wanted to take your web application to a whole new level? Well then, look no further, because this Learning Path takes you on a journey to learning all about Elasticsearch, the renowned open source search engine that helps power searches within thousands of websites worldwide, and much more.

Elasticsearch is part of the Elastic family, popularly called as the Elastic stack, whose other components include Logstash, Kibana, the Beats family, and X-Pack.

Together, the Elastic stack forms an essential suite of tools that is a must for any developer wanting to embark on a path to build high-quality web applications in this day and age. Elasticsearch is a search server that can also double up as a NoSQL data store, and hence provides lightning-fast search functionality within a website. Logstash is used to collect and parse all kinds of logs. It can also be used to ferry data to and from Elasticsearch at high speeds. Kibana is an Elasticsearch data visualization tool, Beats help in gathering data from disparate sources to Elasticsearch, while X-Pack provides services such as security, monitoring, alerting, reporting, and so on.

The Road to Elasticsearch is Packt’s Video Learning Path that is a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it.

First, this learning path gets you acquainted with the new, Elastic stack. You learn all about the key components of the Elastic family, their usage and their significance. Then, we move on to a more detailed topic in which we learn in depth about the new Elasticsearch 5.0, which is the mainstay of the stack. We begin by learning about the fundamentals of Elasticsearch. Here, we learn how data is stored in Elasticsearch, specifically, concepts like index, types, and documents, and are also introduced to the Elasticsearch domain-specific language (DSL). Finally, we learn to create complex search queries that power advanced search features in top websites.

By the end of this Learning Path , you will have developed a mastery of Elasticsearch fundamentals, and would be able to seamlessly harness the power of Elasticsearch to augment the capability of your web apps.

The goal of this Learning Path is to equip you with strong fundamentals of Elasticsearch and introduce you to the Elastic stack.

This Learning Path is authored by some of the best in the field.

Ethan Anthony

Ethan Anthony is a San Francisco-based data scientist who specializes in distributed data-centric technologies, and is also the founder of XResults, a data analytics company. Ethan has over 10 combined years of experience in cloud-based technologies such as Amazon Web Services and OpenStack, as well as the data-centric technologies of Hadoop, Mahout, Spark, and Elasticsearch. He began using Elasticsearch in 2011 and has since delivered solutions based on the Elastic stack to a broad range of clientele.

Karthik Selvaraj

Karthik Selvaraj is an integration specialist having vast experience in areas of enterprise application integration, service-oriented architecture, and API economy. He is a YouTuber and has several training videos on his YouTube channel. His technology stack includes IBM DataPower Gateway, IBM WebSphere MQ, Mule ESB, Elastic stack, Active MQ, and IBM Integration Bus.

A basic knowledge of JavaScript and HTML is required. Also, some familiarity with HTTP methods would be needed

All about the Elastic stack, its major components, their use cases, the installation process

Basic usage of each major component, and its purpose

Success stories for customers by implementing the Elastic stack

Develop a complete data pipeline using the Elastic stack

Core fundamentals and concepts of Elasticsearch

Use RESTful API to interact with data stored in Elasticsearch

Learn to use the Elasticsearch domain-specific language to formulate complex queries for enabling fast searches

Use Elasticsearch with Logstash and Kibana in greater detail

How to perform a full analysis on Apache web logs, with Elasticsearch, Logstash, and Kibana

Learn the differences between Apache Solr and Elasticsearch