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010   $a3-031-69366-3
024 7 $a10.1007/978-3-031-69366-3
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035   $a(DE-He213)978-3-031-69366-3
035   $a(EXLCZ)9936403438800041
100   $a20241026d2024      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aBig Data Infrastructure Technologies for Data Analytics $eScaling Data Science Applications for Continuous Growth /$fby Yuri Demchenko, Juan J. Cuadrado-Gallego, Oleg Chertov, Marharyta Aleksandrova
205   $a1st ed. 2024.
210  1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2024.
215   $a1 online resource (553 pages)
311   $a3-031-69365-5 
327   $aChapter 1 Introduction. - Chapter 2 Big Data Technologies Foundation: Definition, Reference Architecture, use cases. - Chapter 3 Cloud Computing Foundation: Definition, Reference Architecture, Foundational Technologies, Use cases. - Chapter 4 Cloud and Big Data Service Providers and Platforms. - Chapter 5 Big Data Algorithms, MapReduce and Hadoop ecosystem -- Chapter 6 Streaming Analytics and Spark -- Chapter 7 Data Structures for Big Data, Modern Big Data SQL and NoSQL Databases.-Chapter 8 Enterprise Data Governance and Management -- Chapter 9 Research Data Management -- Chapter 10 Big Data Security and Compliance, Data Privacy Protection -- Chapter 11 Finding Data on the Web, Data sets, Web Scraping, Web API -- Chapter 12 Data Science Projects Management,DataOps, MLOPs -- Chapter13 Data Science Projects Development with Amazon SageMaker -- Chapter 14 Data Validation for Data Science Projects.
330   $aThis book provides a comprehensive overview and introduction to Big Data Infrastructure technologies, existing cloud-based platforms, and tools for Big Data processing and data analytics, combining both a conceptual approach in architecture design and a practical approach in technology selection and project implementation. Readers will learn the core functionality of major Big Data Infrastructure components and how they integrate to form a coherent solution with business benefits. Specific attention will be given to understanding and using the major Big Data platform Apache Hadoop ecosystem, its main functional components MapReduce, HBase, Hive, Pig, Spark and streaming analytics. The book includes topics related to enterprise and research data management and governance and explains modern approaches to cloud and Big Data security and compliance. The book covers two knowledge areas defined in the EDISON Data Science Framework (EDSF): Data Science Engineering and Data Management and Governance and can be used as a textbook for university courses or provide a basis for practitioners for further self-study and practical use of Big Data technologies and competent evaluation and implementation of practical projects in their organizations.
606   $aArtificial intelligence$xData processing
606   $aQuantitative research
606   $aSoftware engineering
606   $aArtificial intelligence
606   $aApplication software
606   $aData Science
606   $aData Analysis and Big Data
606   $aSoftware Engineering
606   $aArtificial Intelligence
606   $aComputer and Information Systems Applications
615  0$aArtificial intelligence$xData processing.
615  0$aQuantitative research.
615  0$aSoftware engineering.
615  0$aArtificial intelligence.
615  0$aApplication software.
615 14$aData Science.
615 24$aData Analysis and Big Data.
615 24$aSoftware Engineering.
615 24$aArtificial Intelligence.
615 24$aComputer and Information Systems Applications.
676   $a005.7
700   $aDemchenko$b Yuri$01439883
701   $aCuadrado-Gallego$b Juan J$01439882
701   $aChertov$b Oleg$01767643
701   $aAleksandrova$b Marharyta$01767644
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910899895203321
996   $aBig Data Infrastructure Technologies for Data Analytics$94213958
997   $aUNINA