Elasticsearch 5.x Cookbook
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
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 8 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 at 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 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 BigData solutions for business intelligence, financial and bank companies all over the world. A lot of his time is spent in teaching how to efficiently use big data solutions (mainly Apache Spark), NoSql datastores (Elasticsearch, HBase, and Accumulo) and related technologies (Scala, Akka, Playframework). He is often called as presenter to Big Data or Scala events. He is an evangelist on Scala and Scala.js (the transcompiler from Scala to JavaScript).
In his spare time, when he is not playing with his children, he likes to work on open source projects. When he was in high school, he started contributing to projects related to the GNOME environment (gtkmm). One of his preferred programming languages is Python, and he wrote one of the first NoSQL backends on Django for MongoDB (Django-MongoDB-engine). In 2010, he began using ElasticSearch to provide search capabilities to some Django e-commerce sites and developed PyES (a Pythonic client for ElasticSearch), as well as the initial part of the ElasticSearch MongoDB river. He is the author of ElasticSearch Cookbook as well as a technical reviewer Elasticsearch Server, Second Edition, Learning Scala Web Development, and the video course, Building a Search Server with ElasticSearch, all of which are published by Packt Publishing
https://www.amazon.com/ElasticSearch-Cookbook-Alberto-Paro/dp/1782166629/ref=sr_1_2?ie=UTF8&qid=1467014252&sr=8-2&keywords=ElasticSearch+Cookbook
https://www.amazon.com/ElasticSearch-Cookbook-Second-Alberto-Paro/dp/1783554835/ref=sr_1_1?ie=UTF8&qid=1467014252&sr=8-1&keywords=ElasticSearch+Cookbook
Over 170 advanced recipes to search, analyze, deploy, manage, and monitor data effectively with Elasticsearch 5.x
About This Book
Who This Book Is For
If you are a developer who wants to get the most out of Elasticsearch for advanced search and analytics, this is the book for you. Some understanding of JSON is expected. If you want to extend Elasticsearch, understanding of Java and related technologies is also required.
What You Will Learn
In Detail
Elasticsearch is a Lucene-based distributed search server that allows users to index and search unstructured content with petabytes of data. This book is your one-stop guide to master the complete Elasticsearch ecosystem.
We’ll guide you through comprehensive recipes on what’s new in Elasticsearch 5.x, showing you how to create complex queries and analytics, and perform index mapping, aggregation, and scripting. Further on, you will explore the modules of Cluster and Node monitoring and see ways to back up and restore a snapshot of an index.
You will understand how to install Kibana to monitor a cluster and also to extend Kibana for plugins. Finally, you will also see how you can integrate your Java, Scala, Python, and Big Data applications such as Apache Spark and Pig with Elasticsearch, and add enhanced functionalities with custom plugins.
By the end of this book, you will have an in-depth knowledge of the implementation of the Elasticsearch architecture and will be able to manage data efficiently and effectively with Elasticsearch.
Style and approach
This book follows a problem-solution approach to effectively use and manage Elasticsearch. Each recipe focuses on a particular task at hand, and is explained in a very simple, easy to understand manner.
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