000 03947nam a22005055i 4500
001 978-3-319-38776-5
003 DE-He213
005 20180206183010.0
007 cr nn 008mamaa
008 160824s2016 gw | s |||| 0|eng d
020 _a9783319387765
_9978-3-319-38776-5
050 4 _aQA76.9.D3
072 7 _aUN
_2bicssc
072 7 _aUMT
_2bicssc
072 7 _aCOM021000
_2bisacsh
082 0 4 _a005.74
_223
100 1 _aSakr, Sherif.
_eauthor.
245 1 0 _aBig Data 2.0 Processing Systems
_h[recurso electrónico] :
_bA Survey /
_cby Sherif Sakr.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2016.
300 _aXV, 102 p. 49 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringerBriefs in Computer Science,
_x2191-5768
505 0 _aChapter 1: Introduction -- Chapter 2: General Purpose Big Data Processing Systems -- Chapter 3: Large Scale Processing of Structured Databases -- Chapter 4: Large Scale Graph Processing Systems -- Chapter 5: Large Scale Stream Processing Systems -- Chapter 6: Conclusions and Outlook.
520 _aThis book provides readers the ?big picture? and a comprehensive survey of the domain of big data processing systems. For the past decade, the Hadoop framework has dominated the world of big data processing, yet recently academia and industry have started to recognize its limitations in several application domains and big data processing scenarios such as the large-scale processing of structured data, graph data and streaming data. Thus, it is now gradually being replaced by a collection of engines that are dedicated to specific verticals (e.g. structured data, graph data, and streaming data). The book explores this new wave of systems, which it refers to as Big Data 2.0 processing systems. After Chapter 1 presents the general background of the big data phenomena, Chapter 2 provides an overview of various general-purpose big data processing systems that allow their users to develop various big data processing jobs for different application domains. In turn, Chapter 3 examines various systems that have been introduced to support the SQL flavor on top of the Hadoop infrastructure and provide competing and scalable performance in the processing of large-scale structured data. Chapter 4 discusses several systems that have been designed to tackle the problem of large-scale graph processing, while the main focus of Chapter 5 is on several systems that have been designed to provide scalable solutions for processing big data streams, and on other sets of systems that have been introduced to support the development of data pipelines between various types of big data processing jobs and systems. Lastly, Chapter 6 shares conclusions and an outlook on future research challenges. Overall, the book offers a valuable reference guide for students, researchers and professionals in the domain of big data processing systems. Further, its comprehensive content will hopefully encourage readers to pursue further research on the subject.
650 0 _aComputer science.
650 0 _aBig data.
650 0 _aDatabase management.
650 0 _aInformation storage and retrieval.
650 1 4 _aComputer Science.
650 2 4 _aDatabase Management.
650 2 4 _aBig Data/Analytics.
650 2 4 _aInformation Storage and Retrieval.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319387758
830 0 _aSpringerBriefs in Computer Science,
_x2191-5768
856 4 0 _zLibro electrónico
_uhttp://148.231.10.114:2048/login?url=http://dx.doi.org/10.1007/978-3-319-38776-5
912 _aZDB-2-SCS
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