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010   $a3-319-38776-6
024 7 $a10.1007/978-3-319-38776-5
035   $a(CKB)3710000000837809
035   $a(EBL)4653483
035   $a(DE-He213)978-3-319-38776-5
035   $a(MiAaPQ)EBC4653483
035   $a(PPN)194802078
035   $a(EXLCZ)993710000000837809
100   $a20160824d2016      u|         0
101 0 $aeng
135   $aur|n|---|||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aBig Data 2.0 Processing Systems $eA Survey /$fby Sherif Sakr
205   $a1st ed. 2016.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016.
215   $a1 online resource (111 p.)
225 1 $aSpringerBriefs in Computer Science,$x2191-5768
300   $aDescription based upon print version of record.
311   $a3-319-38775-8 
320   $aIncludes bibliographical references.
327   $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. .
330   $aThis book provides readers the ?big picture? and a comprehensive survey of the domain of big data processing systems. For the past decade, the Hadoop framework has dominated the world of big data processing, yet recently academia and industry have started to recognize its limitations in several application domains and big data processing scenarios such as the large-scale processing of structured data, graph data and streaming data. Thus, it is now gradually being replaced by a collection of engines that are dedicated to specific verticals (e.g. structured data, graph data, and streaming data). The book explores this new wave of systems, which it refers to as Big Data 2.0 processing systems. After Chapter 1 presents the general background of the big data phenomena, Chapter 2 provides an overview of various general-purpose big data processing systems that allow their users to develop various big data processing jobs for different application domains. In turn, Chapter 3 examines various systems that have been introduced to support the SQL flavor on top of the Hadoop infrastructure and provide competing and scalable performance in the processing of large-scale structured data. Chapter 4 discusses several systems that have been designed to tackle the problem of large-scale graph processing, while the main focus of Chapter 5 is on several systems that have been designed to provide scalable solutions for processing big data streams, and on other sets of systems that have been introduced to support the development of data pipelines between various types of big data processing jobs and systems. Lastly, Chapter 6 shares conclusions and an outlook on future research challenges. Overall, the book offers a valuable reference guide for students, researchers and professionals in the domain of big data processing systems. Further, its comprehensive content will hopefully encourage readers to pursue further research on the subject.
410  0$aSpringerBriefs in Computer Science,$x2191-5768
606   $aDatabase management
606   $aBig data
606   $aInformation storage and retrieval
606   $aDatabase Management$3https://scigraph.springernature.com/ontologies/product-market-codes/I18024
606   $aBig Data/Analytics$3https://scigraph.springernature.com/ontologies/product-market-codes/522070
606   $aInformation Storage and Retrieval$3https://scigraph.springernature.com/ontologies/product-market-codes/I18032
615  0$aDatabase management.
615  0$aBig data.
615  0$aInformation storage and retrieval.
615 14$aDatabase Management.
615 24$aBig Data/Analytics.
615 24$aInformation Storage and Retrieval.
676   $a004
700   $aSakr$b Sherif$4aut$4http://id.loc.gov/vocabulary/relators/aut$0866736
906   $aBOOK
912   $a9910255007703321
996   $aBig Data 2.0 Processing Systems$91953983
997   $aUNINA