Data Integration for Business

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.