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Learning Hunk. A quick, practical guide to rapidly visualizing and analyzing your Hadoop data using Hunk Dmitry Anoshin, Sergey Sheypak

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Learning Hunk. A quick, practical guide to rapidly visualizing and analyzing your Hadoop data using Hunk Dmitry Anoshin, Sergey Sheypak - okladka książki

Learning Hunk. A quick, practical guide to rapidly visualizing and analyzing your Hadoop data using Hunk Dmitry Anoshin, Sergey Sheypak - okladka książki

Autorzy:
Dmitry Anoshin, Sergey Sheypak
Ocena:
Bądź pierwszym, który oceni tę książkę
Stron:
156
Dostępne formaty:
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     ePub
     Mobi

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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.

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O autorach książki

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 in the 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. He is also an active speaker at data conferences and helps people to adopt cloud analytics.
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.

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