Andreas Meier, Michael Kaufmann

SQL & NoSQL Databases (eBook, PDF)

Models, Languages, Consistency Options and Architectures for Big Data Management

• Format: PDF

Produktbeschreibung

This book introduces readers to the field of relational (SQL) and non-relational (NoSQL) databases. The main topics covered are data management, data modeling, query and manipulation languages, consistency, privacy and security, system architectures and multi-user operation. The book also provides an overview of post-relational and non-relational database systems. In addition to classic concepts, important aspects of NoSQL databases are discussed, such as map / reduce, distribution options (fragments, replication), and the CAP theorem (Consistency, Availability, and Partition tolerance). The book will benefit students looking for an introduction to the area of SQL and NoSQL databases, as well as practitioners, helping them better understand the strengths and weaknesses of relational and non-relational approaches and developments in connection with big data applications.

Content

Data Management - Data Modeling - Database Languages - Ensuring Data Consistency - System Architecture - Post-Relational Databases - NoSQL Databases

The authors

Andreas Meier is a former member of the Faculty of Economics and Social Science and was a Professor of Information Technology at the University of Fribourg. He specializes in electronic business, electronic government, and information management. He is member of the GI (Gesellschaft für Informatik), IEEE Computer Society, and ACM.

Michael Kaufmann is a Professor of Data Science and Big Data at the School of Information Technology, Lucerne University of Applied Sciences and Arts. He is also the coordinator of the university's Data Intelligence research team, which develops and studies methods and technologies for intelligent data management.

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Produktdetails

• Verlag: Springer-Verlag GmbH
• Seitenzahl: 229
• Erscheinungstermin: 5. Juli 2019
• Englisch
• ISBN-13: 9783658245498
• Artikelnr.: 57626617

Autorenporträt

Andreas Meier is a former member of the Faculty of Economics and Social Science and was a professor of Information Technology at the University of Fribourg. He specializes in electronic business, electronic government, and information management. He is member of the GI (Gesellschaft für Informatik), IEEE Computer Society, and ACM. After studying music in Vienna, he graduated with a degree in mathematics at the Federal Institute of Technology (ETH) in Zurich, studied his doctorate, and qualified as a university lecture at the Institute of Computer Science. He was a systems engineer at the IBM research lab in San Jose¿, California, director of an international bank, and a member of the executive board of an insurance company.

Michael Kaufmann is a Professor of Data Science and Big Data at the School of Information Technology, Lucerne University of Applied Sciences and Arts. He is also the coordinator of the university¿s Data Intelligence research team, which develops and studies methods and technologies for intelligent data management. Michael Kaufmann studied computer science, law and psychology at the University of Fribourg. With extra-occupational doctoral studies, he received his Ph.D. in computer science on the topic of inductive fuzzy classification in marketing analytics. He worked at PostFinance as a data warehouse poweruser in corporate development; Later on at Mobiliar Insurance as a data architect in the enterprise architecture unit; and as a business analyst at FIVE Informatik AG, where he initiated and led a research project and started teaching as a part time lecturer at Kalaidos University of Applied Science. Since 2014 he has been working at the Lucerne University of Applied Sciences and Arts in teaching and research as a lecturer for databases, where he founded and successfully funded the research team data intelligence.

Inhaltsangabe

Data Management.- Data Modeling.- Database Languages.- Ensuring Data Consistency.- System Architecture.- Post-Relational Databases.- NoSQL Databases.