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Big data : principles and best practices of scalable realtime data systems / Nathan Marz, James Warren.

By: Marz, NathanContributor(s): Warren, JamesMaterial type: TextTextPublication details: Greenwich, Conn. : London : Manning ; Pearson Education [distributor], 2013Description: 425 p. ; 24 cmISBN: 9781617290343 (pbk.) :Subject(s): Database management | Real-time data processing | Computers and ITDDC classification: 005.7'4 Summary: Services like social networks, web analytics, and intelligent e-commerce often need to manage data at a scale too big for a traditional database. As scale and demand increase, so does complexity. Fortunately, scalability and simplicity are not mutually exclusive-rather than using some trendy technology, a different approach is needed. Big data systems use many machines working in parallel to store and process data, which introduces fundamental challenges unfamiliar to most developers. 'Big Data' shows how to build these systems using an architecture that takes advantage of clustered hardware along with new tools designed specifically to capture and analyze web-scale data.
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Item type Current library Home library Shelving location Class number Status Date due Barcode Item reservations
Book Book Paul Hamlyn Library Paul Hamlyn Library Floor 1 005.74 MAR (Browse shelf(Opens below)) Issued 25/11/2024 06710158
Book Book Paul Hamlyn Library Paul Hamlyn Library Floor 1 005.74 MAR (Browse shelf(Opens below)) Available 06676634
Book Book Paul Hamlyn Library Paul Hamlyn Library Floor 1 005.74 MAR (Browse shelf(Opens below)) Issued 25/11/2024 06296238
Book Book Paul Hamlyn Library Paul Hamlyn Library Floor 1 005.74 MAR (Browse shelf(Opens below)) Issued 25/11/2024 06296246
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Services like social networks, web analytics, and intelligent e-commerce often need to manage data at a scale too big for a traditional database. As scale and demand increase, so does complexity. Fortunately, scalability and simplicity are not mutually exclusive-rather than using some trendy technology, a different approach is needed. Big data systems use many machines working in parallel to store and process data, which introduces fundamental challenges unfamiliar to most developers. 'Big Data' shows how to build these systems using an architecture that takes advantage of clustered hardware along with new tools designed specifically to capture and analyze web-scale data.

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