HDInsight Essentials
By Rajesh Nadipalli
Publisher: Packt Publishing
Final Release Date: September 2013
Pages: 122

In Detail

We live in an era in which data is generated with every action and a lot of these are unstructured; from Twitter feeds, Facebook updates, photos and digital sensor inputs. Current relational databases cannot handle the volume, velocity and variations of data. HDInsight gives you the ability to gain the full value of Big Data with a modern, cloud-based data platform that manages data of any size and type, whether structured or unstructured.

A hands-on guide that shows you how to seamlessly store and process Big Data of all types through Microsofts modern data platform; which provides simplicity, ease of management, and an open enterprise-ready Hadoop service all running in the Cloud. You will then learn how to analyze your Hadoop data with PowerPivot, Power View, Excel, and other Microsoft BI tools; thanks to integration with the Microsoft data platform, this will give you a solid foundation to build your own HDInsight solution, both on premise and on Cloud.

Firstly, we will provide an overview of Hadoop and Microsoft Big Data strategy, where HDinsight plays a key role. We will then show you how to set up your HDInsight cluster and take you through the 4 stages of collecting, processing, analysing and reporting. For each of these stages, you will see a practical example with working code.

You will then learn core Hadoop concepts like HDFS and MapReduce. You will also get a closer look at how Microsofts HDInsight leverages Hortonworks Data Platform that uses Apache Hadoop. You will then be guided through Hadoop commands and programming using open source software, such as Hive and Pig with HDInsight. Finally, you will learn to analyze and report using PowerPivot, Power View, Excel, and other Microsoft BI tools.

This guide provides step-by-step instructions on how to build a Big Data solution using HDInsight with open source software, provide useful Excel reports, and open up the full value of HDInsight.

Approach

This book is a fast-paced guide full of step-by-step instructions on how to build a multi-node Hadoop cluster on Windows servers.

Who this book is for

If you are a data architect or developer who wants to understand how to transform your data using open source software, such as MapReduce, Hive, Pig and JavaScript, and also leverage the Windows infrastructure; this book is perfect for you. It is also ideal if you are part of a team who is starting or planning a Hadoop implementation, and you want to understand the key components of Hadoop, and how HDInsight provides added value in administration and reporting.

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4.0

HDInsight Essentials Review

By PavanKN

from Bangalore, India

About Me Developer

Verified Reviewer

Pros

  • Easy to understand
  • Helpful examples
  • Well-written

Cons

  • Not comprehensive enough

Best Uses

  • Intermediate

Comments about oreilly HDInsight Essentials:

I would like to congratulate Mr. Rajesh Nadipalli for publishing

HDInsight Essentials book. The below mentioned are some of my

comments that I feel would make this book indispensable in context of

Windows Azure developer/dev-ops specialist/data manager.

Upon reading the book, I would like to see a chapter for Mahout

Integration with HDInsight. If you are using HDInsight in the cloud

then Mahout comes pre-installed for your use whereas if you are

running a local HDInsight instance on Windows Server you must deploy

Mahout on your own.

I propose the following chapter structure:

Introduction – what is mahout, need and motivation for machine

learning jobs in context of BigData, Installing and setting up

the Mahout in HDInsight

Data transformation using Mahout – how mahout can be used for

data transformation, running machine learning tasks, importing

data from Pig, Hive and exporting the machine learning results

to MS Excel

Case Studies using Mahout – real life scenarios where mahout is

deployed to deliver meaningful results extracted from BigData,

some sample test code



There are plenty of use cases where Mahout is used while working with

big data. Some of the examples include building a recommendation

engine, classification engine, performing market basket analysis,

etc…. The typical process could be like:

1. Provisioning a cluster on Windows Azure (HDInsight)

2. Getting the data for analysis from source (using APIs, torrents,

etc…)

3. Extracting the data we need from the gathered data

4. Writing the mapreduce (depending upon the requirement, number of

map/reduce tasks)

5. Building the machine learning engine using Mahout

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