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101 0 $aeng
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182   $cc$2rdamedia
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200 10$aSQL and NoSQL Databases$b[electronic resource] $eModeling, Languages, Security and Architectures for Big Data Management /$fby Michael Kaufmann, Andreas Meier
205   $a2nd ed. 2023.
210  1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2023.
215   $a1 online resource (XIV, 254 p. 110 illus., 109 illus. in color.) 
311   $a9783031279072 
330   $aThis textbook offers a comprehensive introduction to relational (SQL) and non-relational (NoSQL) databases. The authors thoroughly review the current state of database tools and techniques and examine upcoming innovations. In the first five chapters, the authors analyze in detail the management, modeling, languages, security, and architecture of relational databases, graph databases, and document databases. Moreover, an overview of other SQL- and NoSQL-based database approaches is provided. In addition to classic concepts such as the entity and relationship model and its mapping in SQL database schemas, query languages or transaction management, other aspects for NoSQL databases such as non-relational data models, document and graph query languages (MQL, Cypher), the Map/Reduce procedure, distribution options (sharding, replication) or the CAP theorem (Consistency, Availability, Partition Tolerance) are explained. This 2nd English edition offers a new in-depth introduction to document databases with a method for modeling document structures, an overview of the document-oriented MongoDB query language MQL as well as security and architecture aspects. The topic of database security is newly introduced as a separate chapter and analyzed in detail with regard to data protection, integrity, and transactions. Texts on data management, database programming, and data warehousing and data lakes have been updated. In addition, the book now explains the concepts of JSON, JSON schema, BSON, index-free neighborhood, cloud databases, search engines and time series databases. The book includes more than 100 tables, examples and illustrations, and each chapter offers a list of resources for further reading. It conveys an in-depth comparison of relational and non-relational approaches and shows how to undertake development for big data applications. This way, it benefits students and practitioners working across the broad field of data science and applied information technology.
606   $aDatabase management
606   $aArtificial intelligence?Data processing
606   $aInformation retrieval
606   $aComputer architecture
606   $aDatabase Management
606   $aData Science
606   $aData Storage Representation
615  0$aDatabase management.
615  0$aArtificial intelligence?Data processing.
615  0$aInformation retrieval.
615  0$aComputer architecture.
615 14$aDatabase Management.
615 24$aData Science.
615 24$aData Storage Representation.
676   $a005.74
700   $aKaufmann$b Michael$4aut$4http://id.loc.gov/vocabulary/relators/aut$01065100
702   $aMeier$b Andreas$4aut$4http://id.loc.gov/vocabulary/relators/aut
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a996546842703316
996   $aSQL and NoSQL Databases$93561561
997   $aUNISA