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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.
Anonymous
Posted Thu Jan 09 00:00:00 EST 2014
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
Anonymous
Posted Fri Dec 27 00:00:00 EST 2013
I did not had go time to go through the all the chapters. The initial chapters discuss about general big data need and available Hadoop distributions. Next chapters define how to deploy HDInsight and HDInsight Cluster Adminstration..
Have some examples added and content is followed along those line.
Overview
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 ...