Big data : principles and best practices of scalable real-time data systems / Nathan Marz with James Warren.
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TextPublisher: Shelter Island, NY : Manning, [2015]Copyright date: ©2015Description: xx, 308 pages : illustrations ; 24 cmContent type: text Media type: unmediated Carrier type: volumeISBN: 9781617290343; 1617290343Subject(s): Big data | Database management | Database design | Data mining | Big data | Data mining | Database design | Database management | Massendaten | Datenbanksystem | MassendatenDDC classification: 658.4/038 LOC classification: QA76.9.D3 | M374 2015| Item type | Current library | Call number | Copy number | Status | Notes | Date due | Barcode |
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| QA76.9.D26R62 2011 Database principles : fundamentals of design, implementation, and management / | QA76.9.D3 .E57 2017 Fundamentals of database systems / | QA76.9.D3 .F357 2016 Building database clouds in Oracle 12c / | QA76.9.D3 .M374 2015 Big data : principles and best practices of scalable real-time data systems / | QA76.9.D3 .M374 2015 Big data : principles and best practices of scalable real-time data systems / | QA76.9.D3 .M67 2016 Infrastructure as code : managing servers in the cloud / | QA76.9.D3 .Z94 2018 Database management systems : a business-oriented approach using ORACLE, MySQL and MS Access / |
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.
Teaches readers how to build big data systems using Lambda Architecture, an architecture designed specifically to capture and analyze web-scale data.
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