Big Data: Principles and Best Practices of Scalable Real-Time Data Systems (Record no. 31574)
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|---|---|
| fixed length control field | 02535cam a22002417i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 18767342 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20230323113321.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 150902t20152015nyua 001 0 eng d |
| 010 ## - LIBRARY OF CONGRESS CONTROL NUMBER | |
| LC control number | 2015458165 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9789351198062 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | (OCoLC)ocn909039685 |
| 040 ## - CATALOGING SOURCE | |
| Transcribing agency | s |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 005.74 MAR |
| Edition number | 23 |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Marz, Nathan |
| 245 10 - TITLE STATEMENT | |
| Title | Big Data: Principles and Best Practices of Scalable Real-Time Data Systems |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication, distribution, etc | New Delhi: |
| Name of publisher, distributor, etc | Dreamtech Press, |
| Date of publication, distribution, etc | 2015. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 308p. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | Big Data teaches you to build big data systems using an architecture that takes advantage of clustered hardware along with new tools designed specifically to capture and analyze web-scale data. It describes a scalable, easy-to-understand approach to big data systems that can be built and run by a small team. Following a realistic example, this book guides readers through the theory of big data systems, how to implement them in practice, and how to deploy and operate them once they're built. About the Book Web-scale applications like social networks, real-time analytics, or e-commerce sites deal with a lot of data, whose volume and velocity exceed the limits of traditional database systems. These applications require architectures built around clusters of machines to store and process data of any size, or speed. Fortunately, scale and simplicity are not mutually exclusive. Big Data teaches you to build big data systems using an architecture designed specifically to capture and analyze web-scale data. This book presents the Lambda Architecture, a scalable, easy-to-understand approach that can be built and run by a small team. You'll explore the theory of big data systems and how to implement them in practice. In addition to discovering a general framework for processing big data, you'll learn specific technologies like Hadoop, Storm, and NoSQL databases. This book requires no previous exposure to large-scale data analysis or NoSQL tools. Familiarity with traditional databases is helpful. What's Inside Introduction to big data systems Real-time processing of web-scale data Tools like Hadoop, Cassandra, and Storm Extensions to traditional database skills About the Authors Nathan Marz is the creator of Apache Storm and the originator of the Lambda Architecture for big data systems. James Warren is an analytics architect with a background in machine learning and scientific computing |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Big Data. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Database Management. |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Warren, James |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Book |
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