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SQL and Nosql Databases: Modeling, Languages, Security and Architectures for Big Data Management

SQL and Nosql Databases: Modeling, Languages, Security and Architectures for Big Data Management

          
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About the Book


About the Author: Michael Kaufmann is a professor at the Lucerne University of Applied Sciences and Arts. He teaches database systems and researches technologies for intelligent data management. Michael studied computer science, law and psychology at the University of Fribourg. He worked at PostFinance as a data warehouse power user in enterprise development, later at Mobiliar Insurance as a data architect in enterprise architecture and as a business analyst at FIVE Informatik AG. Parallel to his industrial activities, he earned a doctorate in computer science on the topic of inductive fuzzy classification in marketing analytics. Subsequently, he first taught at the Kalaidos University of Applied Sciences Zurich before moving to the Lucerne School of Engineering & Architecture. Since 2016, he has been working at the Lucerne School of Computer Science.
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 José, California, director of an international bank, and a member of the executive board of an insurance company.


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Product Details
  • ISBN-13: 9783031279072
  • Publisher: Springer International Publishing AG
  • Binding: Paperback
  • Language: English
  • Returnable: Y
  • Sub Title: Modeling, Languages, Security and Architectures for Big Data Management
  • Width: 156 mm
  • ISBN-10: 3031279077
  • Publisher Date: 26 May 2023
  • Height: 234 mm
  • No of Pages: 254
  • Spine Width: 14 mm
  • Weight: 385 gr


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