SQL on Big Data
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SQL on Big Data: Technology, Architecture, and Innovation Sumit Pal Wilmington, Massachusetts, USA ISBN-13 (pbk): 978-1-4842-2246-1 ISBN-13 (electronic): 978-1-4842-2247-8 DOI 10.1007/978-1-4842-2247-8 Library of Congress Control Number: 2016958437 Copyright © 2016 by Sumit Pal This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed
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This chapter discusses the history of SQL on big data and why SQL is so essential for commoditization and adoption of big data in the enterprise. The chapter discusses how SQL on big data has evolved and where it stands today. It discusses why the current breed of relational databases cannot live up to the requirements of volume, speed, variability, and scalability of operations required for data integration and data analytics. As more and more data is becoming available on big data platforms, business analysts, business intelligence (BI) tools, and developers all must have access to it, and SQL on big data provides the best way to solve the access problem. This chapter covers the following: • Why SQL on big data? • SQL on big data goals • SQL on big data landscape—commercial and open source tools • How to choose an SQL on big data The world is generating humongous amount of data. Figure 1-1 shows the amount of data being generated over the Internet every minute. This is just the tip of the iceberg. We do not know how much more data is generated and traverses the Internet in the deep Web
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