AI for Database Developers Mastering Intelligent Systems for Optimized Data Management and Advanced Analytics

AI for Database Developers Mastering Intelligent Systems for Optimized Data Management and Advanced Analytics

Harnessing AI to Revolutionize Data Management and Unlock Advanced Analytics

About the Book

AI is transforming database development, enabling smarter, faster, and more efficient data management. AI for Database Developers explores how artificial intelligence enhances database optimization, query performance, automation, and predictive analytics. Whether you're a database engineer, data scientist, or developer, this book provides practical insights, real-world applications, and cutting-edge techniques to integrate AI into your database workflows.

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    • Databases
    • MySQL

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About the Author

ALBERT TETTEH ADJEI
ALBERT TETTEH ADJEI

Albert Tetteh Adjei is a passionate software engineer from Ghana with a deep interest in emerging technologies and digital transformation. A graduate of Aptech Computer Education, he specializes in software development and has a keen focus on next-generation wireless technologies, artificial intelligence, and cybersecurity. With a strong background in tech, Albert is dedicated to simplifying complex topics for readers, helping them navigate the ever-evolving digital landscape.

Table of Contents

Table of Contents Chapter 1: Introduction to AI and Its Relevance to Database Management ....................................................................................... 11 Chapter 2: Fundamentals of Machine Learning for Database Developers ........................................................................................... 14 Chapter 3: Understanding Neural Networks and Deep Learning ..... 19 Chapter 4: AI Integration in Database Systems ................................. 26 Chapter 5: Data Preparation and Cleaning for AI Models ................. 33 Chapter 6: Feature Engineering and Selection Techniques .............. 40 Chapter 7: Supervised Learning Algorithms for Database Analytics. 47 Chapter 8: Unsupervised Learning and Clustering Methods ............ 52 Chapter 9: Implementing Regression Analysis in Databases............. 56 Chapter 10: Classification Techniques for Predictive Analytics ........ 66 Chapter 11: Natural Language Processing in Data Management ..... 74 Chapter 12: AI-Driven Data Visualization Techniques ....................... 81 Chapter 13: Real-Time Data Processing with AI................................. 86 Chapter 14: Anomaly Detection and Fraud Prevention Using AI ...... 90 Chapter 15: Building Recommendation Systems with AI .................. 98 Chapter 16: AI for Database Security and Privacy ........................... 103 Chapter 17: Optimizing Query Performance with AI Techniques ... 110 Chapter 18: AI in Data Warehousing and Big Data Analytics .......... 117 Chapter 19: Enhancing Database Scalability with AI Solutions ....... 123 Chapter 20: Advanced Predictive Modeling and Forecasting ......... 130 9 10 Chapter 21: AI-Enhanced Data Backup and Recovery Strategies ... 134 Chapter 22: Implementing AI-Based Data Governance................... 142 Chapter 23: Ethical Considerations in AI for Database Management ............................................................................................................ 146 Chapter 24: Case Studies: Successful AI Applications in Databases 150 Chapter 25: Future Trends in AI and Data Management ................ 154 Chapter 26: Practical AI Tools and Libraries for Database Developers ............................................................................................................ 163 Chapter 27: Building AI Models with Cloud-Based Platforms ......... 168 Chapter 28: Integrating AI with Existing Database Technologies ... 173 Chapter 29: Performance Metrics and Evaluation of AI Models .... 178 Chapter 30: Designing and Implementing AI Strategies for Database Development ..................................................................................... 184

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