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Learn how to define strategies for cloud adoption of your Oracle database landscape. Understand private cloud, public cloud, and hybrid cloud computing in order to successfully design and manage databases in the cloud. The Cloud DBA-Oracle provides an overview of Database-as-a-Service (DBaaS) that you can use in defining your cloud adoption strategy. In-depth details of various cloud service providers for Oracle database are given, including Oracle Cloud and Amazon Web Services (AWS). Database administration techniques relevant to hosting databases in the cloud are shown in the book as well as the technical details needed to perform all database administration tasks and activities, such as migration to the cloud, backup in the cloud, and new database setup in the cloud. You will learn from real-world business cases and practical examples of administration of Oracle database in the cloud, highlighting the challenges faced and solutions implemented. With this book you will learn: Cloud computing concepts from the DBA perspective, such as private cloud, public cloud, and hybrid cloud Technical details of all aspects of cloud database administration Challenges faced during setup of databases in private cloud or database migration to public cloud Key points to be kept in mind during database administration in the cloud Practical examples of successful Oracle database cloud migration and support.

Database management. --- Information retrieval. --- Computer architecture. --- Database Management. --- Data Storage Representation. --- Oracle (Computer file)

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This book explores the big data evolution by interrogating the notion that big data is a disruptive innovation that appears to be challenging existing epistemologies in the humanities and social sciences. Exploring various (controversial) facets of big data such as ethics, data power, and data justice, the book attempts to clarify the trajectory of the epistemology of (big) data-driven science in the humanities and social sciences.

Sociology & anthropology --- big data --- data storage --- statistical analysis --- digital revolution --- social sciences --- data generation --- data acquisition --- data analysis --- data science

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This 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.

Database management. --- Artificial intelligence—Data processing. --- Information retrieval. --- Computer architecture. --- Database Management. --- Data Science. --- Data Storage Representation.

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Information storage and retrieval systems. --- Automatic data storage --- Automatic information retrieval --- Automation in documentation --- Computer-based information systems --- Data processing systems --- Data storage and retrieval systems --- Discovery systems, Information --- Information discovery systems --- Information processing systems --- Information retrieval systems --- Machine data storage and retrieval --- Mechanized information storage and retrieval systems --- Computer systems --- Electronic information resources --- Data centers --- Digital libraries --- Information organization --- Information retrieval

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Information retrieval --- informatieverwerking --- Documentatie --- Documentation --- Information storage and retrieval systems --- Information storage and retrieval systems. --- Désherbage --- Automatic data storage --- Automatic information retrieval --- Automation in documentation --- Computer-based information systems --- Data processing systems --- Data storage and retrieval systems --- Discovery systems, Information --- Information discovery systems --- Information processing systems --- Information retrieval systems --- Machine data storage and retrieval --- Mechanized information storage and retrieval systems --- Computer systems --- Electronic information resources --- Data libraries --- Digital libraries --- Information organization --- Deselectie --- Literatuuronderzoek --- Documentatie. --- Literatuuronderzoek. --- Data centers