Learning Elasticsearch

Store, search, and analyze your data with ease using Elasticsearch 5.x About This Book Get to grips with the basics of Elasticsearch concepts and its APIs, and use them to create efficient applications Create large-scale Elasticsearch clusters and perform analytics using aggregation This comprehensive guide will get you up and running with Elasticsearch 5.x in no time Who This Book Is For If you want to build efficient search and analytics applications using Elasticsearch, this book is for you. It will also benefit developers who have worked with Lucene or Solr before and now want to work with Elasticsearch. No previous knowledge of Elasticsearch is expected. What You Will Learn See how to set up and configure Elasticsearch and Kibana Know how to ingest structured and unstructured data using Elasticsearch Understand how a search engine works and the concepts of relevance and scoring Find out how to query Elasticsearch with a high degree of performance and scalability Improve the user experience by using autocomplete, geolocation queries, and much more See how to slice and dice your data using Elasticsearch aggregations. Grasp how to use Kibana to explore and visualize your data Know how to host on Elastic Cloud and how to use the latest X-Pack features such as Graph and Alerting In Detail Elasticsearch is a modern, fast, distributed, scalable, fault tolerant, and open source search and analytics engine. You can use Elasticsearch for small or large applications with billions of documents. It is built to scale horizontally and can handle both structured and unstructured data. Packed with easy-to- follow examples, this book will ensure you will have a firm understanding of the basics of Elasticsearch and know how to utilize its capabilities efficiently. You will install and set up Elasticsearch and Kibana, and handle documents using the Distributed Document Store. You will see how to query, search, and index your data, and perform aggregation-based analytics with ease. You will see how to use Kibana to explore and visualize your data. Further on, you will learn to handle document relationships, work with geospatial data, and much more, with this easy-to-follow guide. Finally, you will see how you can set up and scale your Elasticsearch clusters in production environments. Style and approach This comprehensive guide will get you started with Elasticsearch 5.x, so you build a solid understanding of the basics. Every topic is explained in depth and is supplemented with practical examples to enhance your understanding.

A step-by-step guide that will teach you how to use Elasticsearch in your application effectively KEY FEATURES ● Get familiar with the core concepts of Elasticsearch. ● Understand how the search engine works and how Elasticsearch is different from other similar tools. ● Learn to install Elasticsearch on different operating systems. ● Get familiar with the components of Elastic Stack such as Kibana, Logstash, and Beats, etc. ● Learn how to import data from different sources such as RDBMS, and files, etc DESCRIPTION In the modern Information Technology age, we are flooded with loads of data so we should know how to handle those data and transform them to fetch meaningful information. This book is here to help you manage the data using Elasticsearch. The book starts by covering the fundamentals of Elasticsearch and the concept behind it. After the introduction, you will learn how to install Elasticsearch on different platforms. You will then get to know about Index Management where you will learn to create, update, and delete Elasticsearch indices. Then you will understand how the Query DSL works and how to write some complex search queries using the Query DSL. After completing these basic features, you will move to some advanced topics. Under advanced topics, you will learn to handle Geodata which can be used to plot the data on a map. The book then focuses on Data Analysis using Aggregation. You will then learn how to tune Elasticsearch performance. The book ends with a chapter on Elasticsearch administration. What you will learn ● Learn how to create and manage a cluster ● Work with different components of Elastic Stack ● Review the list of top Information Security certifications. ● Get to know more about Elasticsearch Index Management. ● Understand how to improve the performance by tuning Elasticsearch WHO THIS BOOK IS FOR This book is for developers, architects, DBA, DevOps, and other readers who want to learn Elasticsearch efficiently and want to apply that in their application whether it is a new one or an existing one. It is also beneficial to those who want to play with their data using Elasticsearch. Basic computer programming is a prerequisite. TABLE OF CONTENTS 1 Getting started with Elasticsearch 2 Installation Elasticsearch 3 Working with Elastic Stack 4 Preparing your data 5 Importing Data into Elasticsearch 6 Managing Your Index 7 Apply Search on Your Data 8 Handling Geo with Elasticsearch 9 Aggregating Your Data 10 Improving the Performance 11 Administer Elasticsearch

Discover expert techniques for combining machine learning with the analytic capabilities of Elastic Stack and uncover actionable insights from your data Key Features: Integrate machine learning with distributed search and analytics Preprocess and analyze large volumes of search data effortlessly Operationalize machine learning in a scalable, production-worthy way Book Description: Elastic Stack, previously known as the ELK stack, is a log analysis solution that helps users ingest, process, and analyze search data effectively. With the addition of machine learning, a key commercial feature, the Elastic Stack makes this process even more efficient. This updated second edition of Machine Learning with the Elastic Stack provides a comprehensive overview of Elastic Stack's machine learning features for both time series data analysis as well as for classification, regression, and outlier detection. The book starts by explaining machine learning concepts in an intuitive way. You'll then perform time series analysis on different types of data, such as log files, network flows, application metrics, and financial data. As you progress through the chapters, you'll deploy machine learning within Elastic Stack for logging, security, and metrics. Finally, you'll discover how data frame analysis opens up a whole new set of use cases that machine learning can help you with. By the end of this Elastic Stack book, you'll have hands-on machine learning and Elastic Stack experience, along with the knowledge you need to incorporate machine learning in your distributed search and data analysis platform. What You Will Learn: Find out how to enable the ML commercial feature in the Elastic Stack Understand how Elastic machine learning is used to detect different types of anomalies and make predictions Apply effective anomaly detection to IT operations, security analytics, and other use cases Utilize the results of Elastic ML in custom views, dashboards, and proactive alerting Train and deploy supervised machine learning models for real-time inference Discover various tips and tricks to get the most out of Elastic machine learning Who this book is for: If you're a data professional looking to gain insights into Elasticsearch data without having to rely on a machine learning specialist or custom development, then this Elastic Stack machine learning book is for you. You'll also find this book useful if you want to integrate machine learning with your observability, security, and analytics applications. Working knowledge of the Elastic Stack is needed to get the most out of this book.

Whether you need full-text search or real-time analytics of structured data—or both—the Elasticsearch distributed search engine is an ideal way to put your data to work. This practical guide not only shows you how to search, analyze, and explore data with Elasticsearch, but also helps you deal with the complexities of human language, geolocation, and relationships. If you’re a newcomer to both search and distributed systems, you’ll quickly learn how to integrate Elasticsearch into your application. More experienced users will pick up lots of advanced techniques. Throughout the book, you’ll follow a problem-based approach to learn why, when, and how to use Elasticsearch features. Understand how Elasticsearch interprets data in your documents Index and query your data to take advantage of search concepts such as relevance and word proximity Handle human language through the effective use of analyzers and queries Summarize and group data to show overall trends, with aggregations and analytics Use geo-points and geo-shapes—Elasticsearch’s approaches to geolocation Model your data to take advantage of Elasticsearch’s horizontal scalability Learn how to configure and monitor your cluster in production

A beginner's guide to storing, managing, and analyzing data with the updated features of Elastic 7.0 Key FeaturesGain access to new features and updates introduced in Elastic Stack 7.0Grasp the fundamentals of Elastic Stack including Elasticsearch, Logstash, and KibanaExplore useful tips for using Elastic Cloud and deploying Elastic Stack in production environmentsBook Description The Elastic Stack is a powerful combination of tools for techniques such as distributed search, analytics, logging, and visualization of data. Elastic Stack 7.0 encompasses new features and capabilities that will enable you to find unique insights into analytics using these techniques. This book will give you a fundamental understanding of what the stack is all about, and help you use it efficiently to build powerful real-time data processing applications. The first few sections of the book will help you understand how to set up the stack by installing tools, and exploring their basic configurations. You’ll then get up to speed with using Elasticsearch for distributed searching and analytics, Logstash for logging, and Kibana for data visualization. As you work through the book, you will discover the technique of creating custom plugins using Kibana and Beats. This is followed by coverage of the Elastic X-Pack, a useful extension for effective security and monitoring. You’ll also find helpful tips on how to use Elastic Cloud and deploy Elastic Stack in production environments. By the end of this book, you’ll be well versed with the fundamental Elastic Stack functionalities and the role of each component in the stack to solve different data processing problems. What you will learnInstall and configure an Elasticsearch architectureSolve the full-text search problem with ElasticsearchDiscover powerful analytics capabilities through aggregations using ElasticsearchBuild a data pipeline to transfer data from a variety of sources into Elasticsearch for analysisCreate interactive dashboards for effective storytelling with your data using KibanaLearn how to secure, monitor and use Elastic Stack’s alerting and reporting capabilitiesTake applications to an on-premise or cloud-based production environment with Elastic StackWho this book is for This book is for entry-level data professionals, software engineers, e-commerce developers, and full-stack developers who want to learn about Elastic Stack and how the real-time processing and search engine works for business analytics and enterprise search applications. Previous experience with Elastic Stack is not required, however knowledge of data warehousing and database concepts will be helpful.

Master the intricacies of Elasticsearch 7.0 and use it to create flexible and scalable search solutions Key FeaturesMaster the latest distributed search and analytics capabilities of Elasticsearch 7.0Perform searching, indexing, and aggregation of your data at scaleDiscover tips and techniques for speeding up your search query performanceBook Description Building enterprise-grade distributed applications and executing systematic search operations call for a strong understanding of Elasticsearch and expertise in using its core APIs and latest features. This book will help you master the advanced functionalities of Elasticsearch and understand how you can develop a sophisticated, real-time search engine confidently. In addition to this, you'll also learn to run machine learning jobs in Elasticsearch to speed up routine tasks. You'll get started by learning to use Elasticsearch features on Hadoop and Spark and make search results faster, thereby improving the speed of query results and enhancing the customer experience. You'll then get up to speed with performing analytics by building a metrics pipeline, defining queries, and using Kibana for intuitive visualizations that help provide decision-makers with better insights. The book will later guide you through using Logstash with examples to collect, parse, and enrich logs before indexing them in Elasticsearch. By the end of this book, you will have comprehensive knowledge of advanced topics such as Apache Spark support, machine learning using Elasticsearch and scikit-learn, and real-time analytics, along with the expertise you need to increase business productivity, perform analytics, and get the very best out of Elasticsearch. What you will learnPre-process documents before indexing in ingest pipelinesLearn how to model your data in the real worldGet to grips with using Elasticsearch for exploratory data analysisUnderstand how to build analytics and RESTful servicesUse Kibana, Logstash, and Beats for dashboard applicationsGet up to speed with Spark and Elasticsearch for real-time analyticsExplore the basics of Spring Data Elasticsearch, and understand how to index, search, and query in a Spring applicationWho this book is for This book is for Elasticsearch developers and data engineers who want to take their basic knowledge of Elasticsearch to the next level and use it to build enterprise-grade distributed search applications. Prior experience of working with Elasticsearch will be useful to get the most out of this book.

This book is one of its own kind. It is not an encyclopedia or a hands-on tutorial that traps readers in the tutorial hell. It is a distillation of just one common Python user’s learning experience. The experience is packaged with exceptional teaching techniques, careful dependence unraveling and, most importantly, passion. Learning Advanced Python by Studying Open Source Projects helps readers overcome the difficulty in their day-to-day tasks and seek insights from solutions in famous open source projects. Different from a technical manual, this book mixes the technical knowledge, real-world applications and more theoretical content, providing readers with a practical and engaging approach to learning Python. Throughout this book, readers will learn how to write Python code that is efficient, readable and maintainable, covering key topics such as data structures, algorithms, object-oriented programming and more. The author’s passion for Python shines through in this book, making it an enjoyable and inspiring read for both beginners and experienced programmers.

Discover expert techniques for combining machine learning with the analytic capabilities of Elastic Stack and uncover actionable insights from your data Key FeaturesIntegrate machine learning with distributed search and analyticsPreprocess and analyze large volumes of search data effortlesslyOperationalize machine learning in a scalable, production-worthy wayBook Description Elastic Stack, previously known as the ELK stack, is a log analysis solution that helps users ingest, process, and analyze search data effectively. With the addition of machine learning, a key commercial feature, the Elastic Stack makes this process even more efficient. This updated second edition of Machine Learning with the Elastic Stack provides a comprehensive overview of Elastic Stack's machine learning features for both time series data analysis as well as for classification, regression, and outlier detection. The book starts by explaining machine learning concepts in an intuitive way. You'll then perform time series analysis on different types of data, such as log files, network flows, application metrics, and financial data. As you progress through the chapters, you'll deploy machine learning within Elastic Stack for logging, security, and metrics. Finally, you'll discover how data frame analysis opens up a whole new set of use cases that machine learning can help you with. By the end of this Elastic Stack book, you'll have hands-on machine learning and Elastic Stack experience, along with the knowledge you need to incorporate machine learning in your distributed search and data analysis platform. What you will learnFind out how to enable the ML commercial feature in the Elastic StackUnderstand how Elastic machine learning is used to detect different types of anomalies and make predictionsApply effective anomaly detection to IT operations, security analytics, and other use casesUtilize the results of Elastic ML in custom views, dashboards, and proactive alertingTrain and deploy supervised machine learning models for real-time inferenceDiscover various tips and tricks to get the most out of Elastic machine learningWho this book is for If you’re a data professional looking to gain insights into Elasticsearch data without having to rely on a machine learning specialist or custom development, then this Elastic Stack machine learning book is for you. You'll also find this book useful if you want to integrate machine learning with your observability, security, and analytics applications. Working knowledge of the Elastic Stack is needed to get the most out of this book.