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Learning Hunk : visualize and analyze your Hadoop data using Hunk /
Annotation
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, UK :
Packt Publishing,
2015.
|
| Colección: | Community experience distilled.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Anoshin, Dmitry, |e author. | |
| 245 | 1 | 0 | |a Learning Hunk : |b visualize and analyze your Hadoop data using Hunk / |c Dmitry Anoshin, Sergey Sheypak. |
| 264 | 1 | |a Birmingham, UK : |b Packt Publishing, |c 2015. | |
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a Community experience distilled | |
| 588 | 0 | |a Online resource; title from cover page (Safari, viewed January 19, 2016). | |
| 500 | |a Includes index. | ||
| 520 | 8 | |a Annotation |b Visualize and analyze your Hadoop data using HunkAbout 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 efficientlyWho This Book Is ForIf 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 scalabilityIn DetailHunk 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. | |
| 505 | 0 | |a Cover; Copyright; Credits; About the Authors; About the Reviewer; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Meet Hunk; Big data analytics; The big problem; The elegant solution; Supporting SPL; Intermediate results; Getting to know Hunk; Splunk versus Hunk; Hunk architecture; Connecting to Hadoop; Advance Hunk deployment; Native versus virtual indexes; Native indexes; Virtual index; External result provider; Computation models; Data streaming; Data reporting; Mixed mode; Hunk security; One Hunk user to one Hadoop user; Many Hunk users to one Hadoop user. | |
| 505 | 8 | |a Hunk user(s) to the same Hadoop user with different queuesSetting up Hadoop; Starting and using a virtual machine with CDH5; SSH user; MySQL; Starting the VM and cluster in VirtualBox; Big data use case; Importing data from RDBMS to Hadoop using Sqoop; Telecommunications -- SMS, Call, and Internet dataset from dandelion.eu; Milano grid map; CDR aggregated data import process; Periodical data import from MySQL using Sqoop and Oozie; Problems to solve; Summary; Chapter 2: Explore Hadoop Data with Hunk; Setting up Hunk; Extracting Hunk to a VM; Setting up Hunk variables and configuration files. | |
| 505 | 8 | |a Running Hunk for the first timeSetting up a data provider and virtual index for CDR data; Setting up a connection to Hadoop; Setting up a virtual index for data stored in Hadoop; Accessing data through a virtual index; Exploring data; Creating reports; The top five browsers report; Top referrers; Site errors report; Creating alerts; Creating a dashboard; Controlling security with Hunk; The default Hadoop security; One Hunk user to one Hadoop user; Summary; Chapter 3: Meeting Hunk Features; Knowledge objects; Field aliases; Calculated fields; Field extractions; Tags; Event type. | |
| 505 | 8 | |a Workflow actionsMacros; Data model; Add auto-extracting fields; Adding GeoIP attributes; Other ways to add attributes; Introducing Pivot; Summary; Chapter 4: Adding Speed to Reports; Big data performance issues; Hunk report acceleration; Creating a virtual index; Streaming mode; Creating an acceleration search; What's going on in Hadoop?; Report acceleration summaries; Reviewing summary details; Managing report accelerations; Hunk accelerations limits; Summary; Chapter 5: Customizing Hunk; What we are going to do with the Splunk SDK; Supported languages; Solving problems; REST API. | |
| 505 | 8 | |a The implementation planThe conclusion; Dashboard customization using Splunk Web Framework; Functionality; A description of time-series aggregated CDR data; Source data; Creating a virtual index for Milano CDR; Creating a virtual index for the Milano grid; Creating a virtual index using sample data; Implementation; Querying the visualization; Downloading the application; Custom Google Maps; Page layout; Linear gradients and bins for the activity value; Custom map components; Other components; The final result; Summary; Chapter 6: Discovering Hunk Integration Apps; What is Mongo?; Installation. | |
| 590 | |a eBooks on EBSCOhost |b EBSCO eBook Subscription Academic Collection - Worldwide | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 630 | 0 | 0 | |a Apache Hadoop. |
| 630 | 0 | 7 | |a Apache Hadoop |2 fast |
| 650 | 0 | |a Big data. | |
| 650 | 0 | |a Information visualization. | |
| 650 | 0 | |a Non-relational databases. | |
| 650 | 6 | |a Données volumineuses. | |
| 650 | 6 | |a Visualisation de l'information. | |
| 650 | 6 | |a Bases de données non relationnelles. | |
| 650 | 7 | |a COMPUTERS |x Computer Literacy. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Computer Science. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Data Processing. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Hardware |x General. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Information Technology. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Machine Theory. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Reference. |2 bisacsh | |
| 650 | 7 | |a Big data |2 fast | |
| 650 | 7 | |a Information visualization |2 fast | |
| 650 | 7 | |a Non-relational databases |2 fast | |
| 700 | 1 | |a Sheypak, Sergey, |e author. | |
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| 776 | 0 | 8 | |i Print version: |a Anoshin, Dmitry. |t Learning Hunk. |d Birmingham : Packt Publishing, ©2015 |
| 830 | 0 | |a Community experience distilled. | |
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