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Introductory, theory-practice balanced text teaching the fundamentals of databases to advanced undergraduates or graduate students in information systems or computer science.
Every day the demand for a good database management system is increasing as information is growing and expanding faster than ever. This book aims to provide detail coverage of all the topics related to database design, its use and implementation. It incorporates all basic terminology of Database and its applications. It starts with basic database architecture and concludes with advanced topics like security and recovery.
Easy-to-read writing style. Comprehensive coverage of all database topics. Bullet lists and tables. More detailed examples of database implementations. More SQL, including significant information on planned revisions to the language. Simple and easy explanation to complex topics like relational algebra, relational calculus, query processing and optimization. Covers topics on implementation issues like security, integrity, transaction management, concurrency control, backup and recovery etc. Latest advances in database technology.
Defining a set of guiding principles for data management and describing how these principles can be applied within data management functional areas; Providing a functional framework for the implementation of enterprise data management practices; including widely adopted practices, methods and techniques, functions, roles, deliverables and metrics; Establishing a common vocabulary for data management concepts and serving as the basis for best practices for data management professionals. DAMA-DMBOK2 provides data management and IT professionals, executives, knowledge workers, educators, and researchers with a framework to manage their data and mature their information infrastructure, based on these principles: Data is an asset with unique properties; The value of data can be and should be expressed in economic terms; Managing data means managing the quality of data; It takes metadata to manage data; It takes planning to manage data; Data management is cross-functional and requires a range of skills and expertise; Data management requires an enterprise perspective; Data management must account for a range of perspectives; Data management is data lifecycle management; Different types of data have different lifecycle requirements; Managing data includes managing risks associated with data; Data management requirements must drive information technology decisions; Effective data management requires leadership commitment.