- Taschenbuch: 464 Seiten
- Verlag: Database & Erp - Omg (1. Oktober 2013)
- Sprache: Englisch
- ISBN-10: 0071827269
- ISBN-13: 978-0071827263
- Größe und/oder Gewicht: 22,4 x 2,3 x 22,9 cm
- Durchschnittliche Kundenbewertung: Schreiben Sie die erste Bewertung
- Amazon Bestseller-Rang: Nr. 305.372 in Fremdsprachige Bücher (Siehe Top 100 in Fremdsprachige Bücher)
Oracle Big Data Handbook (Oracle Press) (Englisch) Taschenbuch – 1. Oktober 2013
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Über den Autor und weitere Mitwirkende
Tom Plunkett is a Senior Sales Consultant with Oracle. Tom also teaches graduate-level computer science courses for Virginia Tech as an adjunct instructor and distance learning instructor. Tom helped win several industry awards for a big data project that Oracle and the Frederick National Laboratory for Cancer Research collaborated on to analyze relationships between genomes and cancer subtypes, including the 2012 Government Big Data Solution Award, ACT-IAC finalist for best pilot/start-up project, and was nominated for a 2013 Computer World Honor Award for Innovation. Tom has spoken internationally at over 40 conferences on the subject of Big Data since leading a team that won a Big Data project from the Office of the Secretary of Defense in 2009. Tom is the lead author of several books, including Oracle Big Data Handbook and Oracle Exalogic Elastic Cloud Handbook. Previously, Tom worked for IBM and practiced patent law for Fliesler Meyer. Tom has a BA and a JD from George Mason University, and an MS in computer science from Virginia Tech.
Brian Macdonald is a Distinguished Solution Consultant and certified Oracle Enterprise Architect with Oracle. He has more than 20 years of experience creating architectures and implementing analytic platforms to address a wide range of customer needs including data warehousing, business intelligence, OLAP, Hadoop, Master Data Management, and ETL technologies.
Bruce Nelson is the Oracle Big Data lead for the Western U.S. and has more than 24 years of experience in the IT industry with a focus on Hadoop, noSQL, Oracle Database, Oracle RAC, and Oracle Exadata.
Mark Hornick is a Director in the Oracle Database Advanced Analytics group focusing on Oracle R Enterprise (ORE), Oracle R Connector for Hadoop (ORCH), and Oracle R Distribution (ORD). He also works with internal and external customers in the application of R for scalable applications in Oracle Database, Exadata, and the Big Data Appliance, also engaging in SAS-to-R conversion and performance benchmarking. Mark is co-author of Java Data Mining: Strategy, Standard, and Practice. He joined Oracle's Data Mining Technologies group in 1999 through the acquisition of Thinking Machines Corp. Mark was a founding member of and currently serves as an Oracle Advisor to the IOUG Business Intelligence Warehousing and Analytics (BIWA) SIG. He has conducted training sessions on R, ORE, and ORCH in the US, EMEA, APAC, and has presented at conference, including Oracle OpenWorld, Collaborate, BIWA Summit, and the R user conference useR! Mark holds a bachelor's degree from Rutgers University and a master's degree from Brown University, both in computer science.
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I have been associated with and worked at Oracle for a long time. Besides the excellent technology that Oracle produces, I continue to be amazed and impressed by Oracle's ability to repeatedly adapt to new trends and embrace new concepts and technologies to deliver compelling data management solutions to the market. This is perhaps the most important reason for Oracle's enduring success in the market. Perhaps, this is also the most important reason why anyone interested in big data should read this book. If you are a user of Oracle technologies, you'll learn about how to extend your Oracle investment to encompass big data processing. If you're not an Oracle user, you might discover important technologies and approaches to enhance your big data projects. In either case, you will benefit!
This book is about Oracle's big data offerings. As usual, Oracle has delivered a well thought-out, well integrated and comprehensive solution to the market and the authors have done a stellar job of describing Oracle's big data solutions. Processing big data involves several complementary technologies, each requiring different skills and expertise. This is partly reflected in the long list of authors for this book - no single person could have done justice to the topic of big data. EAch author has done a masterful job of describing the product or topic in his area of expertise and describing how these components relate to each other.
The book is an excellent resource for a very relevant, present day topic. Though big data encompasses a lot of complementary technologies, the authors have done a great job of organizind the material into the introductory section, followed by chapters that discuss the important technology components individually and in-depth, followed by a multi-chapter discussion of how to use these technologies to analyze data in various ways. The last section of the book covers important issues like privacy, security and big data governance. The book is organized so that it can be read from the beginning to end to get a complete and comprehensive understanding of Oracle's big data offerings; on the other hand, each chapter is relatively self-contained and can be read "standalone" if the reader chooses.
I liked the big data appliance chapter because it highlights the benefits of pre-built, tested, optimized and production-ready engineered systems for big data processing. In my opinion, big data processing is a complex topic along multiple dimensions including data processing expertise, server configuration and management, optimizing data flows and computation in a distributed environment. The big data appliance minimizes the headaches associated with building and maintaining servers, which can be a great help in getting a big data project up and running quickly and efficiently.
What sets big data processing apart from other kinds of processing is the variety, volume and velocity of data as well as the plethora of analytic techniques needed in order to derive valuable information. The book describes the various data management platforms available, as well as the different kinds of analyses that make up big data processing, including discovery analytics, text and data analytics, spatial analytics and graph analytics.
The book also outlines how one might go about using big data processing in an enterprise, starting with collecting data from diverse sources, doing "experiments" to understand how that data might be used, and then using those results to drive business decisions. Of course, this is an iterative process, where each iteration results in new understanding of the data.
Written by a team of experts, this book provides a comprehensive and complete guide to the big data practitioner. Definitely a must read for anyone serious about big data.
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