Data Integrity A Comprehensive Guide to Safeguarding Information Across Modern Technologies, Cloud Environments, and AI

This book begins by defining what data integrity is and why it matters, before taking readers through its historical context and evolution in technology. Readers will gain insights into the types of data integrity, such as physical, logical, entity, and referential integrity, and how these concepts are applied in modern database management systems, software development, and cloud computing.
The book delves deep into challenges posed by distributed systems, big data, and AI, as well as exploring practical strategies for ensuring data integrity in high-stakes industries like finance and healthcare. Real-world case studies, tools, and best practices offer readers actionable insights into how to maintain and improve data integrity within their organizations.
The final chapters focus on the future of data integrity, examining emerging technologies like AI and quantum computing, and what they mean for data accuracy and security. As organizations embrace digital transformation, Data Integrity equips professionals, developers, and data managers with the knowledge and tools they need to safeguard their data in a rapidly evolving landscape.