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Big data : principles and best practices of scalable real-time data systems /

By: Marz, Nathan [author.].
Contributor(s): Warren, James [author.].
Publisher: Shelter Island, NY : Manning, 2015Description: xx, 308 pages : illustrations ; 24 cm.Content type: text | text | still image Media type: unmediated | unmediated Carrier type: volume | volumeISBN: 9781617290343; 9781617290343:; 1617290343.Subject(s): Big data | Database management | Real-time data processing | Database design | Data miningDDC classification: 006.312
Contents:
A new paradigm for big data -- Data model for big data -- Data model for big data : illustration -- Data storage on the batch layer -- Data storage on the batch layer : illustration -- Batch layer -- Batch layer : illustration -- An example batch layer : architecture and algorithms -- An example batch layer : implementation -- Serving layer -- Serving layer : illustration -- Realtime views -- Realtime views : illustration -- Queuing and stream processing -- Queuing and stream processing : illustration -- Micro-batch stream processing -- Micro-batch stream processing : illustration -- Lambda Architecture in depth.
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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Holdings
Item type Current library Call number Copy number Status Date due Barcode Item holds
Standard Loan Standard Loan ATU Sligo Yeats Library Main Lending Collection 006.312 MAR (Browse shelf(Opens below)) 1 Available 0092638
Total holds: 0

Includes index.

A new paradigm for big data -- Data model for big data -- Data model for big data : illustration -- Data storage on the batch layer -- Data storage on the batch layer : illustration -- Batch layer -- Batch layer : illustration -- An example batch layer : architecture and algorithms -- An example batch layer : implementation -- Serving layer -- Serving layer : illustration -- Realtime views -- Realtime views : illustration -- Queuing and stream processing -- Queuing and stream processing : illustration -- Micro-batch stream processing -- Micro-batch stream processing : illustration -- Lambda Architecture in depth.

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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