Integrate Elasticsearch into Hadoop to effectively visualize and analyze your data About This Book Build production-ready analytics applications by integrating the Hadoop ecosystem with Elasticsearch Learn complex Elasticsearch queries and develop real-time monitoring Kibana dashboards to visualize your data Use Elasticsearch and Kibana to search data in Hadoop easily with this comprehensive, step-by-step guide Who This Book Is For This book is targeted at Java developers with basic knowledge on Hadoop. No prior Elasticsearch experience is expected. What You Will Learn Set up the Elasticsearch-Hadoop environment Import HDFS data into Elasticsearch with MapReduce jobs Perform full-text search and aggregations efficiently using Elasticsearch Visualize data and create interactive dashboards using Kibana Check and detect anomalies in streaming data using Storm and Elasticsearch Inject and classify real-time streaming data into Elasticsearch Get production-ready for Elasticsearch-Hadoop based projects Integrate with Hadoop eco-system such as Pig, Storm, Hive, and Spark In Detail The Hadoop ecosystem is a de-facto standard for processing terra-bytes and peta-bytes of data. Lucene-enabled Elasticsearch is becoming an industry standard for its full-text search and aggregation capabilities. Elasticsearch-Hadoop serves as a perfect tool to bridge the worlds of Elasticsearch and Hadoop ecosystem to get best out of both the worlds. Powered with Kibana, this stack makes it a cakewalk to get surprising insights out of your massive amount of Hadoop ecosystem in a flash. In this book, you'll learn to use Elasticsearch, Kibana and Elasticsearch-Hadoop effectively to analyze and understand your HDFS and streaming data. You begin with an in-depth understanding of the Hadoop, Elasticsearch, Marvel, and Kibana setup. Right after this, you will learn to successfully import Hadoop data into Elasticsearch by writing MapReduce job in a real-world example. This is then followed by a comprehensive look at Elasticsearch essentials, such as full-text search analysis, queries, filters and aggregations; after which you gain an understanding of creating various visualizations and interactive dashboard using Kibana. Classifying your real-world streaming data and identifying trends in it using Storm and Elasticsearch are some of the other topics that we'll cover. You will also gain an insight about key concepts of Elasticsearch and Elasticsearch-hadoop in distributed mode, advanced configurations along with some common configuration presets you may need for your production deployments. You will have “Go production checklist” and high-level view for cluster administration for post-production. Towards the end, you will learn to integrate Elasticsearch with other Hadoop eco-system tools, such as Pig, Hive and Spark. Style and approach A concise yet comprehensive approach has been adopted with real-time examples to help you grasp the concepts easily.

Frank Kane's hands-on Spark training course, based on his bestselling Taming Big Data with Apache Spark and Python video, now available in a book. Understand and analyze large data sets using Spark on a single system or on a cluster. About This Book Understand how Spark can be distributed across computing clusters Develop and run Spark jobs efficiently using Python A hands-on tutorial by Frank Kane with over 15 real-world examples teaching you Big Data processing with Spark Who This Book Is For If you are a data scientist or data analyst who wants to learn Big Data processing using Apache Spark and Python, this book is for you. If you have some programming experience in Python, and want to learn how to process large amounts of data using Apache Spark, Frank Kane's Taming Big Data with Apache Spark and Python will also help you. What You Will Learn Find out how you can identify Big Data problems as Spark problems Install and run Apache Spark on your computer or on a cluster Analyze large data sets across many CPUs using Spark's Resilient Distributed Datasets Implement machine learning on Spark using the MLlib library Process continuous streams of data in real time using the Spark streaming module Perform complex network analysis using Spark's GraphX library Use Amazon's Elastic MapReduce service to run your Spark jobs on a cluster In Detail Frank Kane's Taming Big Data with Apache Spark and Python is your companion to learning Apache Spark in a hands-on manner. Frank will start you off by teaching you how to set up Spark on a single system or on a cluster, and you'll soon move on to analyzing large data sets using Spark RDD, and developing and running effective Spark jobs quickly using Python. Apache Spark has emerged as the next big thing in the Big Data domain – quickly rising from an ascending technology to an established superstar in just a matter of years. Spark allows you to quickly extract actionable insights from large amounts of data, on a real-time basis, making it an essential tool in many modern businesses. Frank has packed this book with over 15 interactive, fun-filled examples relevant to the real world, and he will empower you to understand the Spark ecosystem and implement production-grade real-time Spark projects with ease. Style and approach Frank Kane's Taming Big Data with Apache Spark and Python is a hands-on tutorial with over 15 real-world examples carefully explained by Frank in a step-by-step manner. The examples vary in complexity, and you can move through them at your own pace.

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

Data science teams looking to turn research into useful analytics applications require not only the right tools, but also the right approach if they’re to succeed. With the revised second edition of this hands-on guide, up-and-coming data scientists will learn how to use the Agile Data Science development methodology to build data applications with Python, Apache Spark, Kafka, and other tools. Author Russell Jurney demonstrates how to compose a data platform for building, deploying, and refining analytics applications with Apache Kafka, MongoDB, ElasticSearch, d3.js, scikit-learn, and Apache Airflow. You’ll learn an iterative approach that lets you quickly change the kind of analysis you’re doing, depending on what the data is telling you. Publish data science work as a web application, and affect meaningful change in your organization. Build value from your data in a series of agile sprints, using the data-value pyramid Extract features for statistical models from a single dataset Visualize data with charts, and expose different aspects through interactive reports Use historical data to predict the future via classification and regression Translate predictions into actions Get feedback from users after each sprint to keep your project on track

Over 170 advanced recipes to search, analyze, deploy, manage, and monitor data effectively with Elasticsearch 5.x About This Book Deploy and manage simple Elasticsearch nodes as well as complex cluster topologies Write native plugins to extend the functionalities of Elasticsearch 5.x to boost your business Packed with clear, step-by-step recipes to walk you through the capabilities of Elasticsearch 5.x 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 Choose the best Elasticsearch cloud topology to deploy and power it up with external plugins Develop tailored mapping to take full control of index steps Build complex queries through managing indices and documents Optimize search results through executing analytics aggregations Monitor the performance of the cluster and nodes Install Kibana to monitor cluster and extend Kibana for plugins Integrate Elasticsearch in Java, Scala, Python and Big Data applications 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.

Learn advanced analytical techniques and leverage existing tool kits to make your analytic applications more powerful, precise, and efficient. This book provides the right combination of architecture, design, and implementation information to create analytical systems that go beyond the basics of classification, clustering, and recommendation. Pro Hadoop Data Analytics emphasizes best practices to ensure coherent, efficient development. A complete example system will be developed using standard third-party components that consist of the tool kits, libraries, visualization and reporting code, as well as support glue to provide a working and extensible end-to-end system. The book also highlights the importance of end-to-end, flexible, configurable, high-performance data pipeline systems with analytical components as well as appropriate visualization results. You'll discover the importance of mix-and-match or hybrid systems, using different analytical components in one application. This hybrid approach will be prominent in the examples. What You'll Learn Build big data analytic systems with the Hadoop ecosystem Use libraries, tool kits, and algorithms to make development easier and more effective Apply metrics to measure performance and efficiency of components and systems Connect to standard relational databases, noSQL data sources, and more Follow case studies with example components to create your own systems Who This Book Is For Software engineers, architects, and data scientists with an interest in the design and implementation of big data analytical systems using Hadoop, the Hadoop ecosystem, and other associated technologies.

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.