Springer Handbook of Data Engineering

Inbunden, Engelska, 2026

6 195 kr

Kommande

Beskrivning

The Springer Handbook of Data Engineering 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.

Produktinformation

Utgivningsdatum:2026-10-28
Mått:210 x 279 x undefined mm
Format:Inbunden
Språk:Engelska
Serie:Springer Handbooks
Antal sidor:1 123
Förlag:Springer International Publishing AG
ISBN:9783031923715

Utforska kategorier

Nätverk och kommunikation inom Data och IT
Databaser inom Data och IT
Artificiell intelligens inom Data och IT

Mer om författaren

Prof. Dr. Fatos Xhafa is Full Professor (Catedràtic d'Universitat) at the Dept. of Computer Science, Universitat Politècnica de Catalunya, Barcelona, Spain. He received his PhD in Computer Science from the Dept. of CS at BarcelonaTech in 1998. He has held various academic positions at BarcelonaTech and short term positions abroad including Visiting Professorship at University of Surrey and at the University of London, UK and a Research Associate at Drexel University, USA. He is a member of the IMP-Information Modelling Processing Research Group of UPC.

Innehållsförteckning

PART A: Networking Data.- Part B: Data Analytics.- Part C: HPC Big Data and Cloud Applications.- Part D: Health Data.- Part E: Finance Data.- Part F: Quality of Service, Smart Contracts and Blockchain.- Part G: Sustainable Land Management.- Part H: Data Piracy, Data Integreation, Architectures and Services.- Part I: Data Security.- Part J: Digital Twins and Virtual Reality.- Part K: Data Quality, Data Lineage/Data Governance.

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Beskrivning

Produktinformation

Utgivningsdatum:2026-10-28
Mått:210 x 279 x undefined mm
Format:Inbunden
Språk:Engelska
Serie:Springer Handbooks
Antal sidor:1 123
Förlag:Springer International Publishing AG
ISBN:9783031923715

Utforska kategorier

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Databaser inom Data och IT
Artificiell intelligens inom Data och IT

Mer om författaren

Innehållsförteckning

PART A: Networking Data.- Part B: Data Analytics.- Part C: HPC Big Data and Cloud Applications.- Part D: Health Data.- Part E: Finance Data.- Part F: Quality of Service, Smart Contracts and Blockchain.- Part G: Sustainable Land Management.- Part H: Data Piracy, Data Integreation, Architectures and Services.- Part I: Data Security.- Part J: Digital Twins and Virtual Reality.- Part K: Data Quality, Data Lineage/Data Governance.