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.
In this video, we will have a brief introduction about all the product offerings of Elastic and how each product fits into the stack.
In this video, we will discuss the data analytics.
In this video,you will know how to install Elasticsearch in a system running the Windows operating system.
In this video, you will know how to install Logstash and Kibana in a system running the Windows operating system.
In this video, we will have a look at the basic concepts of Elasticsearch.
In this video, we will have an overview on the Elasticsearch Restful APIs.
In this video,you will get toknow what Query DSL and mapping are in Elasticsearch.
In this video, we will discuss how to perform aggregations in Elasticsearch.
In this video, we will be discussing Elasticsearch analyzers.
In this video, we will discuss how to use scripting in Elasticsearch.
In this video, we will have a look at theLogstash plugins.
In this video, we will have a look at the monitoring APIs of logstash.
In this video, we will have a look at how to build a Kibana interface.
In this video we will look at how to visualize data using kibana.
In this video,you will know how to build dashboards using Kibana.
In this video, we will takea look at the features of Kibana 5.
In this video, we will be walking through a practical implementation of Elastic Stack by setting the input stage.
In this video, we will be walking through a practical implementation of Elastic Stack by implementing the filter stage.
In this video, we will be walking through a practical implementation of Elastic Stack by loading the documents to Elasticsearch.
In this video, we will be visualizing the data using Kibana.
In this video, we will discuss why Netflix implemented Elastic Stack and how it benefitted them.
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.
To get a feel for the course we do an end-to-end overview of what will be covered.
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.
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.
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.
Version 5.0 of ElasticSearch has some key changes. Highlighting these changes help developers to get a better understanding of what's new.
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.
The indices are easily the cornerstone of ElasticSearch. As such, understanding indices and how they work is key.
Documents hold data in ElasticSearch. Understanding the workings of documents put one on the path to better understanding ElasticSearch.
The concept of a cluster can be broad and sometimes confusing. Understanding an ElasticSearch cluster is an important step in the learning process.
Distributed technology is extremely challenging to understand. Understanding how to set shards and replicas in ElasticSearch is therefore a necessary first step.
Index and Mapping set the stage for data search and analysis. Knowledge of how each work is important for effective ElasticSearch usage.
Document addition and deletion in ElasticSearch controls the flow of data. The ability to add and delete documents in ElasticSearch is necessary.
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.
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.
Using REST API requires knowledge of how to run desired queries. Hands-on experience makes this possible.
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.
Accessing the power of ElasticSearch necessitates understanding of its query language, DSL. A breakdown of DSL and how it works is essential.
Moving beyond the basics in DSL can be challenging. Going beyond the basic to take a deeper look into DSL helps.
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.
Sometimes you will want to search a range of values. Range queries are the solution to this challenge.
At times you will need to know when a given field exists. This is when you should turn to exist queries.
Generating analytics can be a challenging task. Built-in aggregation based analytics in ElasticSearch take the pain out of analytics.
The process of running aggregation based analytics in ElasticSearch can get confusing. Experience is the best teacher.
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.
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.
It's not enough to know the steps to the log analysis process. Practical experience is also necessary for true understanding.
In the world of data management, sorting is an absolute must have feature. Learning to sort in ElasticSearch can greatly improve search results.
The ability to query Geo data highly ranks in modern use cases. ElasticSearch simplifies the process of geo searching.
User generated queries for text search can be filled with colloquialism, abbreviations etc. Synonyms in ElasticSearch aim to help minimize this challenge.
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