Dan Sullivan - NoSQL for Mere Mortals

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Dan Sullivan

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Library of Congress Control Number: 2015935038

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ISBN-13: 978-0-13-402321-2
ISBN-10: 0-13-402321-8

Text printed in the United States on recycled paper at Edwards Brothers Malloy, Ann Arbor, Michigan.

First printing, April 2015

Editor-in-Chief: Greg Wiegand
Acquisitions Editor: Joan Murray
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For Katherine

Dan Sullivan is a data architect and data scientist with more than 20 years of experience in business intelligence, machine learning, data mining, text mining, Big Data, data modeling, and application design. Dans project work has ranged from analyzing complex genomics and proteomics data to designing and implementing numerous database applications. His most recent work has focused on NoSQL database modeling, data analysis, cloud computing, text mining, and data integration in life sciences. Dan has extensive experience in relational database design and works regularly with NoSQL databases. Dan has presented and written extensively on NoSQL, cloud computing, analytics, data warehousing, and business intelligence. He has worked in many industries, including life sciences, financial services, oil and gas, manufacturing, health care, insurance, retail, power systems, telecommunications, pharmaceuticals, and publishing.

Whatever there be of progress in life comes not through adaptation but through daring.

HENRY MILLER

It is difficult to avoid discussions about data. Individuals are concerned about keeping their personal data private. Companies struggle to keep data out of the hands of cybercriminals. Governments and businesses have an insatiable appetite for data. IT analysts trip over themselves coming up with new terms to describe data: Big Data, streaming data, high-velocity data, and unstructured data. There is no shortage of terms for ways to store data: databases, data stores, data warehouses, and data lakes. Someone has gone so far as to coin the phrase data swamp.

While others engage in sometimes heated discussions about data, there are those who need to collect, process, analyze, and manage data. This book is for them.

NoSQL databases emerged from unmet needs. Data management tools that worked well for decades could not keep up with demands of Internet applications. Hundreds and thousands of business professionals using corporate databases were no longer the most challenging use case. Companies such as Google, Amazon, Facebook, and Yahoo! had to meet the needs of users that measured in the millions.

The theoretically well-grounded relational data model that had served us so well needed help. Specialized applications, like Web crawling and online shopping cart management, motivated the enhancement and creation of nonrelational databases, including key-value, document, column family, and graph databases. Relational databases are still needed and face no risk of being replaced by NoSQL databases. Instead, NoSQL databases offer additional options with different performance and functional characteristics.