Learning Path: The Road to Elasticsearch
- 5 hours on-demand video
- 1 downloadable resource
- Full lifetime access
- Access on mobile and TV
- Certificate of Completion
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- 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
In this video, we will have a look at the basic concepts of Elasticsearch.
In this video, we will have a look at how to build a Kibana interface.
In this video, we will be walking through a practical implementation of Elastic Stack by setting the input stage.
In this video, we will discuss why Netflix implemented Elastic Stack and how it benefitted them.
To get a feel for the course we do an end-to-end overview of what will be covered.
The indices are easily the cornerstone of ElasticSearch. As such, understanding indices and how they work is key.
Index and Mapping set the stage for data search and analysis. Knowledge of how each work is important for effective ElasticSearch usage.
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.
Accessing the power of ElasticSearch necessitates understanding of its query language, DSL. A breakdown of DSL and how it works is essential.
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.
ElasticSearch alone doesn't provide security, cluster management, log analysis and so on. Thus, ElasticStack was created.
Log analysis is a multi-step process that requires attention to detail. Clear understanding of the process is essential.
In the world of data management, sorting is an absolute must have feature. Learning to sort in ElasticSearch can greatly improve search results.
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.
- 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.