Elasticsearch For Hadoop
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Elasticsearch for Hadoop
Author: Vishal Shukla
language: en
Publisher: Packt Publishing Ltd
Release Date: 2015-10-27
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.
Agile Data Science 2.0
Author: Russell Jurney
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2017-06-07
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
Business Data Analytics
This book constitutes the proceedings of the Second International Conference on Business Data Analytics , ICBDA 2023 held in Dehradun, India, in December 7–8, 2023. The 28 full papers presented together were carefully reviewed and selected from 130 submissions. They focus on all aspects of businesses to familiarize and operate strategic firms and talent supervision skills, diabetes data analysis, predictive analysis with a focus on future trend forecasting, approximation theory, control theory, and signal processing, AI-powered drones use computer vision to recognize, classify, and track objects, etc.