Michael Manoochehri - Data Just Right Introduction to Large-Scale Data & Analytics

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Introduction to Large-Scale Data & Analytics

Michael Manoochehri

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Library of Congress Cataloging-in-Publication Data

Manoochehri, Michael.
Data just right : introduction to large-scale data & analytics / Michael Manoochehri.
pages cm
Includes bibliographical references and index.
ISBN 978-0-321-89865-4 (pbk. : alk. paper)ISBN 0-321-89865-6 (pbk. : alk. paper)
1. Database design. 2. Big data. I. Title.
QA76.9.D26M376 2014
005.743dc23
2013041476

Copyright 2014 Pearson Education, Inc.

All rights reserved. Printed in the United States of America. This publication is protected by copyright, and permission must be obtained from the publisher prior to any prohibited reproduction, storage in a retrieval system, or transmission in any form or by any means, electronic, mechanical, photocopying, recording, or likewise. To obtain permission to use material from this work, please submit a written request to Pearson Education, Inc., Permissions Department, One Lake Street, Upper Saddle River, New Jersey 07458, or you may fax your request to (201) 236-3290.

ISBN-13: 978-0-321-89865-4
ISBN-10: 0-321-89865-6
Text printed in the United States on recycled paper at RR Donnelley in Crawfordsville, Indiana.
First printing, December 2013

This book is dedicated to my parents,
Andrew and Cecelia Manoochehri,
who put everything they had into making sure
that I received an amazing education.

The array of tools for collecting, storing, and gaining insight from data is huge and getting bigger every day. For people entering the field, that means digging through hundreds of Web sites and dozens of books to get the basics of working with data at scale. Thats why this book is a great addition to the Addison-Wesley Data & Analytics series; it provides a broad overview of tools, techniques, and helpful tips for building large data analysis systems.

Michael is the perfect author to provide this introduction to Big Data analytics. He worked on the Cloud Platform Developer Relations team at Google, helping developers with BigQuery, Googles hosted platform for analyzing terabytes of data quickly. He brings his breadth of experience to this book, providing practical guidance for anyone looking to start working with Big Data or anyone looking for additional tips, tricks, and tools.

The introductory chapters start with guidelines for success with Big Data systems and introductions to NoSQL, distributed computing, and the CAP theorem. An introduction to analytics at scale using Hadoop and Hive is followed by coverage of real-time analytics with BigQuery. More advanced topics include MapReduce pipelines, Pig and Cascading, and machine learning with Mahout. Finally, youll see examples of how to blend Python and R into a working Big Data tool chain. Throughout all of this material are examples that help you work with and learn the tools. All of this combines to create a perfect book to read for picking up a broad understanding of Big Data analytics.

Paul Dix, Series Editor

Did you notice? Weve recently crossed a threshold beyond which mobile technology and social media are generating datasets larger than humans can comprehend. Large-scale data analysis has suddenly become magic.

The growing fields of distributed and cloud computing are rapidly evolving to analyze and process this data. An incredible rate of technological change has turned commonly accepted ideas about how to approach data challenges upside down, forcing companies interested in keeping pace to evaluate a daunting collection of sometimes contradictory technologies.

Relational databases, long the drivers of business-intelligence applications, are now being joined by radical NoSQL open-source upstarts, and features from both are appearing in new, hybrid database solutions. The advantages of Web-based computing are driving the progress of massive-scale data storage from bespoke data centers toward scalable infrastructure as a service. Of course, projects based on the open-source Hadoop ecosystem are providing regular developers access to data technology that has previously been only available to cloud-computing giants such as Amazon and Google.

The aggregate result of this technological innovation is often referred to as Big Data. Much has been made about the meaning of this term. Is Big Data a new trend, or is it an application of ideas that have been around a long time? Does Big Data literally mean lots of data, or does it refer to the process of approaching the value of data in a new way? George Dyson, the historian of science, summed up the phenomena well when he said that Big Data exists when the cost of throwing away data is more than the machine cost. In other words, we have Big Data when the value of the data itself exceeds that of the computing power needed to collect and process it.