Xhafa Springer Handbook of Data Engineering

Erscheinungsjahr 2026
ISBN: 978-3-031-92372-2
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 1152 Seiten

Reihe: Springer Handbooks

The is a comprehensive reference on the principles, technologies, and practices for designing, building, and deploying modern data infrastructures. It addresses the engineering foundations required to transform massive, heterogeneous data into actionable knowledge for intelligent systems and data-driven decision-making.

This handbook explores the full spectrum of data engineering challenges, from distributed architectures and cloud-based processing to security, governance, and emerging applications. Thus, the handbook supports the creation and operationalization of modern AI tools, which depend on high-quality, secure, and scalable data pipelines. By integrating diverse aspects of modern data ecosystems into a single comprehensive volume, this handbook establishes itself as a unique reference in the field of data engineering.

The content is organized into eleven parts, covering networking data and the foundations of distributed systems, advanced data analytics techniques, and high-performance computing for big data processing in cloud environments. It examines specialized domains such as health data and finance data, and addresses critical topics including quality of service, smart contracts, and blockchain technologies. Further sections explore sustainable land management through data-driven approaches, as well as issues of data piracy, integration, architectures, and services. Security is treated in depth, alongside emerging concepts such as digital twins and virtual reality. Finally, the handbook provides comprehensive coverage of data quality, lineage, and governance to ensure integrity and compliance in complex data ecosystems.

With contributions from leading experts, it combines theoretical depth with practical insights, making it an indispensable resource for academics, researchers, and professionals.

Zielgruppe

Research

Autoren/Hrsg.

Xhafa, Fatos

Inhaltsverzeichnis

PART I: Networking Data.- Part II: Data Analytics.- Part III: HPC Big Data Processing in Cloud Environments..- Part IV: Health Data.- Part V: Finance Data.- Part VI: Quality of Service, Smart Contracts and Blockchain.- Part VII: Sustainable Land Management.- Part VIII: Data Piracy, Data Integreation, Architectures and Services.- Part IX: Data Security.- Part X: Digital Twins and Virtual Reality.- Part XI: Data Quality, Data Lineage/Data Governance.

Über Autor(innen)

Fatos Xhafa is Full Professor at the Technical University of Catalonia (UPC), Barcelona, Spain. After receiving his PhD from the Department of Computer Science at BarcelonaTech in 1998, he held various tenured and visiting professorship positions. He was a Visiting Professor at the University of Surrey, UK (2019/2020), Visiting Professor at the Birkbeck College, University of London, UK (2009/2010), and a Research Associate at Drexel University, Philadelphia, USA (2004/2005). He was a Distinguished Guest Professor at Hubei University of Technology, China, for the duration of three years (2016–2019). 

Prof. Xhafa has widely published in peer-reviewed international journals, conferences/workshops, book chapters, edited books, and proceedings in the field (H-index 64). He has been awarded teaching and research merits by the Spanish Ministry of Science and Education and by IEEE conferences and best paper awards. He has an extensive editorial service of journals and book volumes. He is founder of the journal ; (Scopus and Clarivate WoS Science Citation Index) and of the , (Emerging Sources Citation Index), and Board Member of several other indexed international journals.

Fatos Xhafa is a member of IEEE Communications Society, IEEE Systems, and Man & Cybernetics Society and Founder Member of Emerging Technical Subcommittee of Internet of Things. His research interests include IoT and Cloud-to-thing continuum computing, massive data processing and collective intelligence, optimization, security and trustworthy computing, and machine learning, among others.