Big Data Analytics with Microsoft Hdinsight in 24 Hours, Sams Teach Yourself

by Singh, Manpreet
3.5 out of 5 Customer Rating
ISBN: 9780672337277
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$9.99

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Overview

Sams Teach Yourself Big Data Analytics with Microsoft HDInsight in 24 Hours


In just 24 lessons of one hour or less, Sams Teach Yourself Big Data Analytics with Microsoft HDInsight in 24 Hours helps you leverage Hadoop's power on a flexible, scalable cloud platform using Microsoft's newest business intelligence, visualization, and productivity tools.


This book's straightforward, step-by-step approach shows you how to provision, configure, monitor, and troubleshoot HDInsight and use Hadoop cloud services to solve real analytics problems. You'll gain more of Hadoop's benefits, with less complexity-even if you're completely new to Big Data analytics. Every lesson builds on what you've already learned, giving you a rock-solid foundation for real-world success.

Practical, hands-on examples show you how to apply what you learn

Quizzes and exercises help you test your knowledge and stretch your skills

Notes and tips point out shortcuts and solutions

Learn how to...

- Master core Big Data and NoSQL concepts, value propositions, and use cases

- Work with key Hadoop features, such as HDFS2 and YARN

- Quickly install, configure, and monitor Hadoop (HDInsight) clusters in the cloud

- Automate provisioning, customize clusters, install additional Hadoop projects, and administer clusters

- Integrate, analyze, and report with Microsoft BI and Power BI

- Automate workflows for data transformation, integration, and other tasks

- Use Apache HBase on HDInsight

- Use Sqoop or SSIS to move data to or from HDInsight

- Perform R-based statistical computing on HDInsight datasets

- Accelerate analytics with Apache Spark

- Run real-time analytics on high-velocity data streams

- Write MapReduce, Hive, and Pig programs

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  • Format: Trade Paperback
  • Author: Singh, Manpreet
  • ISBN: 9780672337277
  • Condition: Used
  • Dimensions: 9.10 x 1.10
  • Number Of Pages: 592
  • Publication Year: 2019

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