Elasticsearch 5.x Solutions – Extending Elasticsearch
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Elasticsearch 5.x Solutions – Extending Elasticsearch

Integrate ElasticSearch with Java, Python, and Scala, and use Plugins to extend its usefulness.
0.0 (0 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
0 students enrolled
Created by Packt Publishing
Last updated 8/2017
English
Current price: $10 Original price: $125 Discount: 92% off
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Includes:
  • 1.5 hours on-demand video
  • 1 Supplemental Resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Creating a client
  • Managing mapping, indices, and documents
  • Executing a search with aggregation
  • Creating a REST and an ingest plugin
  • Indexing data via Apache Spark
View Curriculum
Requirements
  • Some understanding of JSON is expected. If you want to extend Elasticsearch, understanding of Java and related technologies is also required.
Description

Once you’re done with mastering all the capabilities of Elasticsearch, it’s time to go one step beyond. This final part of the Elasticsearch 5.x Solutions series dives into the third-party integration aspect of Elasticsearch. Spanning just under 3 hours, it gives you a detailed coverage of how Elasticsearch can be integrated with popular languages such as Python, Java and Scala, as well as shows you how Elasticsearch is integrated with third party tools for efficient Big Data solutions.

About the Author

Alberto Paro is an engineer, project manager, and software developer. He currently works as freelance trainer/consultant on big data technologies and NoSQL solutions. He loves to study emerging solutions and applications mainly related to big data processing, NoSQL, natural language processing, and neural networks. He began programming in BASIC on a Sinclair Spectrum when he was eight years old, and to date, has collected a lot of experience using different operating systems, applications, and programming languages.

In 2000, he graduated in computer science engineering from Politecnico di Milano with a thesis on designing multiuser and multidevice web applications. He assisted professors at the university for about a year. He then came in contact with The Net Planet Company and loved their innovative ideas; he started working on knowledge management solutions and advanced data mining products. In summer 2014, his company was acquired by a big data technologies company, where he worked until the end of 2015 mainly using Scala and Python on state-of-the-art big data software (Spark, Akka, Cassandra, and YARN).
In 2013, he started freelancing as a consultant for big data, machine learning, Elasticsearch and other NoSQL products. He has created or helped to develop big data solutions for business intelligence, financial, and banking companies all over the world. A lot of his time is spent teaching how to efficiently use big data solutions (mainly Apache Spark), NoSql datastores (Elasticsearch, HBase, and Accumulo) and related technologies (Scala, Akka, and Playframework). He is often called to present at big data or Scala events. He is an evangelist on Scala and Scala.js (the transcompiler from Scala to JavaScript).

Who is the target audience?
  • If you are a developer who wants to get the most out of Elasticsearch for advanced search and analytics, this is the video for you.
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Curriculum For This Course
23 Lectures
01:26:37
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Java Integration
6 Lectures 30:52

This video gives an overview of the entire course.      

Preview 02:38

An HTTP client is one of the easiest clients to create. It's very handy because it allows for the calling, not only of the internal methods as the native protocol does, but also of third-party calls implemented in plugins that can be only called via HTTP.

Creating Clients
08:33

In this video, we will see how to manage indices via client calls.

Managing Indices with the Native Client
02:51

In this video we will see how to manage mappings via a native client, and then use native APIs to manage documents.

Managing Mappings, Documents, and Bulk Actions
07:51

In this video, we will see how to create a query object via QueryBuilder and via simple strings.           

Building a Query
02:20

We can execute a query to retrieve some documents and learn what extra parameter in the query scroll is provided by Elasticsearch.           

Executing Search
06:39
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Scala Integration
6 Lectures 14:18

The first step for working with elastic4s is to create a connection client to call ElasticSearch. Similar to Java, the connection client is native and can be a node or a transport one.

Preview 03:01

After having a client, the first action to do is to create a custom index with an optimized mapping for it. Elastic4s provides a powerful DSL to do this kind of operation.            

Managing Indices
02:17

After creating an index, the next step is to add some mappings to it.

Managing Mappings
01:54

In this video, we will use APIs to manage documents.

Managing Documents
02:56

Elastic4s leverages the query DSL, bringing to Scala a type-safe definition for the queries. One of the most common advantages of this functionality is that, as Elasticsearch evolves, in Scala code via elastic4s. 

Executing a Standard Search
02:08

The elastic4s DSL also provides support for aggregation so that it can be built in a safer typed way.

Executing a Search with Aggregations
02:02
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Python Integration
6 Lectures 20:58

We'll see how to instantiate a client with varying options

Preview 05:46

We will look at how to manage indices via client calls

Managing Indices
03:32

After creating an index, the next step is to add some type mappings to it

Managing Mappings
02:10

We will see how to use APIs to manage a document in a standard way 

Managing Documents
03:22

After inserting documents, the most commonly executed action in Elasticsearch is the search

Executing a Standard Search
03:16

Aggregations are executed along the search by performing analytics on searched results

Executing a Search with Aggregations
02:52
+
Plugin Development
5 Lectures 20:29

We will see how to set up a working environment to develop native plugins

Preview 05:26

We will add a new custom English analyzer similar to the one provided by Elasticsearch

Creating an Analyzer Plugin
03:00

We will see how to define a REST entry-point and create its action; in the next one, we'll see how to execute this action 
distributed in shards

Creating a REST Plugin
04:01

An Elasticsearch action is generally executed and distributed in the cluster, and in this video, we will see how to implement this kind of action

Creating a Cluster Action
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Creating an Ingest Plugin
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About the Instructor
Packt Publishing
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Packt has been committed to developer learning since 2004. A lot has changed in software since then - but Packt has remained responsive to these changes, continuing to look forward at the trends and tools defining the way we work and live. And how to put them to work.

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