Learning Path: The Road to Elasticsearch
3.1 (9 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
88 students enrolled

Learning Path: The Road to Elasticsearch

Learn all you need to know about Elasticsearch and get started with the new Elastic Stack.
3.1 (9 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
88 students enrolled
Created by Packt Publishing
Last updated 5/2017
English [Auto-generated]
Current price: $139.99 Original price: $199.99 Discount: 30% off
5 hours left at this price!
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This course includes
  • 5 hours on-demand video
  • 1 downloadable resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • 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
Course content
Expand all 60 lectures 05:01:52
+ Getting Started with Elastic Stack
25 lectures 02:06:20

This video provides an overview of the entire course.

Preview 04:03

In this video, we will have a brief introduction about all the product offerings of Elastic and how each product fits into the stack.

The Elastic Family

In this video, we will discuss the data analytics.

Making Sense of Your Data

In this video,you will know how to install Elasticsearch in a system running the Windows operating system.

Installing and Starting Elasticsearch

In this video, you will know how to install Logstash and Kibana in a system running the Windows operating system.

Installing and Starting Logstash and Kibana

In this video, we will have a look at the basic concepts of Elasticsearch.

Preview 06:10

In this video, we will have an overview on the Elasticsearch Restful APIs.

Elasticsearch APIs

In this video,you will get toknow what Query DSL and mapping are in Elasticsearch.

Query DSL and Mapping

In this video, we will discuss how to perform aggregations in Elasticsearch.

Aggregations Using Elasticsearch

In this video, we will be discussing Elasticsearch analyzers.

Elasticsearch Analyzers

In this video, we will discuss how to use scripting in Elasticsearch.

Scripting in Elasticsearch

In this video, we will see how to stash events using Logstash.

Preview 08:13

In this video, we will have a look at theLogstash plugins.

Logstash Plugins

In this video, we will have a look at the monitoring APIs of logstash.

APIs for Logstash

In this video, we will have a look at how to build a Kibana interface.

Preview 05:58

In this video we will look at how to visualize data using kibana.

Visualizing Data with Kibana

In this video,you will know how to build dashboards using Kibana.

Building Dashboards with Kibana

In this video, we will takea look at the features of Kibana 5.

Featuring Kibana 5

In this video, we will be walking through a practical implementation of Elastic Stack by setting the input stage.

Preview 02:38

In this video, we will be walking through a practical implementation of Elastic Stack by implementing the filter stage.

Filtering and Processing Input

In this video, we will be walking through a practical implementation of Elastic Stack by loading the documents to Elasticsearch.

Loading Data to Elasticsearch

In this video, we will be visualizing the data using Kibana.

Discover Using Kibana

In this video, we will discuss why Netflix implemented Elastic Stack and how it benefitted them.

Preview 03:50

In this video, we will discuss why Salesforce implemented Elastic Stack and how it benefitted them.


In this video, we will discuss why Orange and Tango implemented Elastic Stack and how it benefitted them.

Orange and Tango
+ Learning Elasticsearch 5.0
35 lectures 02:55:32

To get a feel for the course we do an end-to-end overview of what will be covered.

Preview 03:08

Gaining a holistic view of a new technology is the first necessary step to learning how it works. ElasticSearch is introduced with accompanying use cases.

What Is ElasticSearch?

Setting up a new technology is often a challenging affair. By walking through the simple process of installing ElasticSearch, developers can quickly move along the learning process.

Installing ElasticSearch

Before diving into any new technology, it is all too important to understand what the subject technology was designed for and the best use cases. Peering into the objectives of ElasticSearch solves this.

Goal of ElasticSearch

Version 5.0 of ElasticSearch has some key changes. Highlighting these changes help developers to get a better understanding of what's new.

What's New in Version 5.0?

As is the case with any new technology, developers seek compelling use cases for implementing ElasticSearch. ElasticSearch answers the call by being a fairly straight forward, developer friendly analytics engine.

Why Use ElasticSearch?

The indices are easily the cornerstone of ElasticSearch. As such, understanding indices and how they work is key.

Preview 02:31

Documents hold data in ElasticSearch. Understanding the workings of documents put one on the path to better understanding ElasticSearch.

Documents in ElasticSearch

The concept of a cluster can be broad and sometimes confusing. Understanding an ElasticSearch cluster is an important step in the learning process.

What Is a Cluster?

Distributed technology is extremely challenging to understand. Understanding how to set shards and replicas in ElasticSearch is therefore a necessary first step.

Setting Shards and Replicas

Index and Mapping set the stage for data search and analysis. Knowledge of how each work is important for effective ElasticSearch usage.

Preview 08:09

Document addition and deletion in ElasticSearch controls the flow of data. The ability to add and delete documents in ElasticSearch is necessary.

Adding and Deleting Documents

Since adding documents is among the most commonly performed tasks in ElasticSearch, there needs to be a way to add multiple documents simultaneously. The bulk API solves this problem.

Using Bulk API

Interfacing with technology from external systems can be challenging and often requires a high degree of expertise. The REST API in ElasticSearch solves this problem.

Preview 03:21

Using REST API requires knowledge of how to run desired queries. Hands-on experience makes this possible.

Using REST API to Search

One common point of confusion in REST technology is differentiating between PUT and POST. Gaining a clear understanding of PUT versus POST in updates is therefore key.

Using REST API to Update

Accessing the power of ElasticSearch necessitates understanding of its query language, DSL. A breakdown of DSL and how it works is essential.

Preview 04:10

Moving beyond the basics in DSL can be challenging. Going beyond the basic to take a deeper look into DSL helps.

Understanding DSL

Understanding the type of queries required to gain optimal results is necessary in ElasticSearch. Knowledge of term queries and boosting helps to optimize query results.

Term Queries and Boosting

Sometimes you will want to search a range of values. Range queries are the solution to this challenge.

Range Query

At times you will need to know when a given field exists. This is when you should turn to exist queries.

Exist Query

Generating analytics can be a challenging task. Built-in aggregation based analytics in ElasticSearch take the pain out of analytics.

Aggregation Based Analytics

The process of running aggregation based analytics in ElasticSearch can get confusing. Experience is the best teacher.

Aggregations: Implementation

Not understanding the intended use of a technology can lead to bad implementations or even worse. It is imperative to understand what ElasticSearch is NOT designed for.

Preview 08:39

ElasticSearch alone doesn't provide security, cluster management, log analysis and so on. Thus, ElasticStack was created.

Preview 01:47

Data aggregation is the first of many steps in the analysis process. Kibana facilitates data visualization and acts as a cluster management interface.


Log analysis is an involved process that moves unstructured log data into the ElasticSearch cluster. Logstash was created for this very reason.


Prior to ElasticSearch 5.0, security, monitoring, alerting, reporting, graph and so on. were all separate components. Version 5.0 combined these to form X-Pack.


The complexity of moving data from external systems to ElasticSearch presents many challenges. Beats was created to simplify the process of moving data into ElasticSearch.


Log analysis is a multi-step process that requires attention to detail. Clear understanding of the process is essential.

Preview 05:40

It's not enough to know the steps to the log analysis process. Practical experience is also necessary for true understanding.

Running Log Analysis

In the world of data management, sorting is an absolute must have feature. Learning to sort in ElasticSearch can greatly improve search results.

Preview 04:33

The ability to query Geo data highly ranks in modern use cases. ElasticSearch simplifies the process of geo searching.

Geo Searching

User generated queries for text search can be filled with colloquialism, abbreviations etc. Synonyms in ElasticSearch aim to help minimize this challenge.

Getting into Synonyms

In the world of data management, sorting is an absolute must have feature. Learning to sort in ElasticSearch can greatly improve search results.

Preview 04:45
  • A basic knowledge of JavaScript and HTML is required. Also, some familiarity with HTTP methods would be needed.

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 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 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.

Who this course is for:
  • Any web developer interested in getting started with Elasticsearch seriously, in a big way, by first comprehensively understanding the basics, with an intention of using Elasticsearch heavily in the future.