Search, analyze, and manage data effectively with Elasticsearch 7
Key Features
Book Description
Elasticsearch is a Lucene-based distributed search server that allows users to index and search unstructured content with petabytes of data. With this book, you'll be guided through comprehensive recipes on what's new in Elasticsearch 7, and see how to create and run complex queries and analytics.
Packed with recipes on performing index mapping, aggregation, and scripting using Elasticsearch, this fourth edition of Elasticsearch Cookbook will get you acquainted with numerous solutions and quick techniques for performing both every day and uncommon tasks such as deploying Elasticsearch nodes, integrating other tools to Elasticsearch, and creating different visualizations. You will install Kibana to monitor a cluster and also extend it using a variety of plugins. Finally, you will integrate your Java, Scala, Python, and big data applications such as Apache Spark and Pig with Elasticsearch, and create efficient data applications powered by enhanced functionalities and custom plugins.
By the end of this book, you will have gained in-depth knowledge of implementing Elasticsearch architecture, and you'll be able to manage, search, and store data efficiently and effectively using Elasticsearch.
What you will learn
Who this book is for
If you're a software engineer, big data infrastructure engineer, or Elasticsearch developer, you'll find this book useful. This Elasticsearch book will also help data professionals working in the e-commerce and FMCG industry who use Elastic for metrics evaluation and search analytics to get deeper insights for better business decisions. Prior experience with Elasticsearch will help you get the most out of this book.
Table of Contents
"synopsis" may belong to another edition of this title.
Alberto Paro is an engineer, project manager, and software developer. He currently works as Big Data Practice Leader in NTTDATA in Italy on big data technologies, native cloud, and NoSQL solutions. He loves to study emerging solutions and applications mainly related to cloud and big data processing, NoSQL, NLP, and neural networks. In 2000, he graduated in computer science engineering from Politecnico di Milano. Then, he worked with many companies mainly using Scala/Java and Python on knowledge management solutions and advanced data mining products using the state-of-the-art big data software. A lot of his time is spent teaching how to effectively use big data solutions, NoSQL datastores, and related technologies.
"About this title" may belong to another edition of this title.
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