Data Integration for Business
Practical Strategies for Unifying Relational, Dimensional, and Big Data Systems
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Beschreibung
Data integration is the cornerstone of modern analytics and AI. Organizations today face the challenge of unifying data from diverse systems—relational databases, dimensional warehouses, and key-value big data stores—into coherent, actionable structures. This book provides a comprehensive, practitioner-oriented guide to building integrated data foundations that enable scalable analytics and informed decision-making.
Drawing on best practices and real-world scenarios, the book offers a step-by-step approach to schema alignment, record linkage, and data fusion, while explaining how to design mediated schemas that bridge multiple data models. It addresses critical governance, compliance, and security considerations, ensuring that integrated systems meet regulatory standards and ethical responsibilities. Practical tools and frameworks such as including SQL, Python, Hadoop, Spark, and low-code solutions are presented alongside checklists and case-based examples to help readers operationalize integration strategies effectively.
Whether you are a data architect, analyst, or graduate student in information systems, this book equips you with the knowledge to implement governance and security practices to safeguard data integrity and compliance as well as apply diagnostic tools and best-practice frameworks for sustainable, dynamic data systems.
Andrew Harrison is Associate Professor in the Operations, Business Analytics, and Information Systems Department and Associate Dean in the Carl H. Lindner College of Business at the University of Cincinnati, USA. His research interests include fraud detection and deterrence, computer-mediated communication, knowledge management, and data integration. He is an expert and advocate for developing large-scale data systems that ingest and integrate disparate data sources for business analytics.
Data integration is the cornerstone of modern analytics and AI. Organizations today face the challenge of unifying data from diverse systems—relational databases, dimensional warehouses, and key-value big data stores—into coherent, actionable structures. This book provides a comprehensive, practitioner-oriented guide to building integrated data foundations that enable scalable analytics and informed decision-making.
Drawing on best practices and real-world scenarios, the book offers a step-by-step approach to schema alignment, record linkage, and data fusion, while explaining how to design mediated schemas that bridge multiple data models. It addresses critical governance, compliance, and security considerations, ensuring that integrated systems meet regulatory standards and ethical responsibilities. Practical tools and frameworks such as including SQL, Python, Hadoop, Spark, and low-code solutions are presented alongside checklists and case-based examples to help readers operationalize integration strategies effectively.
Whether you are a data architect, analyst, or graduate student in information systems, this book equips you with the knowledge to implement governance and security practices to safeguard data integrity and compliance as well as apply diagnostic tools and best-practice frameworks for sustainable, dynamic data systems.
Shows readers learn how to design polyglot systems that reflect real-world complexity Shows how hardware, algorithms, and business priorities shape cost-effective data integration strategies Features actionable checklists and an ongoing case study, enabling readers to apply concepts immediately
Autor*in
Andrew Harrison
Themen in »Data Integration for Business«
data system data analytics big data database design data modeling data warehouse big data systems data governance AI readiness