Learning Hunk - Softcover

Anoshin, Dmitry; Sheypak, Sergey

Softcover

ISBN 10: 1782174826 ISBN 13: 9781782174820

Publisher: Packt Publishing, 2015

Synopsis

Visualize and analyze your Hadoop data using Hunk

About This Book

• Explore your data in Hadoop and NoSQL data stores
• Create and optimize your reporting experience with advanced data visualizations and data analytics
• A comprehensive developer's guide that helps you create outstanding analytical solutions efficiently

Who This Book Is For

If you are Hadoop developers who want to build efficient real-time Operation Intelligence Solutions based on Hadoop deployments or various NoSQL data stores using Hunk, this book is for you. Some familiarity with Splunk is assumed.

What You Will Learn

• Deploy and configure Hunk on top of Cloudera Hadoop
• Create and configure Virtual Indexes for datasets
• Make your data presentable using the wide variety of data visualization components and knowledge objects
• Design a data model using Hunk best practices
• Add more flexibility to your analytics solution via extended SDK and custom visualizations
• Discover data using MongoDB as a data source
• Integrate Hunk with AWS Elastic MapReduce to improve scalability

In Detail

Hunk is the big data analytics platform that lets you rapidly explore, analyse, and visualize data in Hadoop and NoSQL data stores. It provides a single, fluid user experience, designed to show you insights from your big data without the need for specialized skills, fixed schemas, or months of development. Hunk goes beyond typical data analysis methods and gives you the power to rapidly detect patterns and find anomalies across petabytes of raw data.

This book focuses on exploring, analysing, and visualizing big data in Hadoop and NoSQL data stores with this powerful full-featured big data analytics platform.

You will begin by learning the Hunk architecture and Hunk Virtual Index before moving on to how to easily analyze and visualize data using Splunk Search Language (SPL). Next you will meet Hunk Apps which can easy integrate with NoSQL data stores such as MongoDB or Sqqrl. You will also discover Hunk knowledge objects, build a semantic layer on top of Hadoop, and explore data using the friendly user-interface of Hunk Pivot. You will connect MongoDB and explore data in the data store. Finally, you will go through report acceleration techniques and analyze data in the AWS Cloud.

Style and approach

A step-by-step guide starting right from the basics and deep diving into the more advanced and technical aspects of Hunk.

About this title

About the Author

Dmitry Anoshin

Dmitry Anoshin is a data-centric technologist and a recognized expert in building and implementing big data and analytics solutions. He has a successful track record when it comes to implementing business and digital intelligence projects in numerous industries, including retail, finance, marketing, and e-commerce. Dmitry possesses in-depth knowledge of digital/business intelligence, ETL, data warehousing, and big data technologies. He has extensive experience in the data integration process and is proficient in using various data warehousing methodologies. Dmitry has constantly exceeded project expectations when he has worked for financial, machine tool, and retail industries. He has completed a number of multinational full BI/DI solution life cycle implementation projects. With expertise in data modeling, Dmitry also has a background and business experience in multiple relation databases, OLAP systems, and NoSQL databases. In addition, he has reviewed SAP BusinessObjects Reporting Cookbook, Creating Universes with SAP BusinessObjects, and Learning SAP BusinessObjects Dashboards, all by Packt Publishing and was the author of SAP Lumira Essentials, Packt Publishing.

Sergey Sheypak

Sergey Sheypak started his so-called big data practice in 2010 as a Teradata PS consultant. His was leading the Teradata Master Data Management deployment in Sberbank, Russia (which has 110 billion customers). Later Sergey switched to AsterData and Hadoop practices. Sergey joined the Research and Development team at MegaFon (one of the top three telecom companies in Russia with 70 billion customers) in 2012. While leading the Hadoop team at MegaFon, Sergey built ETL processes from existing Oracle DWH to HDFS. Automated end-to-end tests and acceptance tests were introduced as a mandatory part of the Hadoop development process. Scoring geospatial analysis systems based on specific telecom data were developed and launched. Now, Sergey works as independent consultant in Sweden.

Edition details

• Publisher: Packt Publishing
• Publication date: 2015
• Language: English
• ISBN 10: 1782174826
• ISBN 13: 9781782174820
• Binding: Paperback
• Number of pages: 156