Table of Content
Introduction
Part One
Foundations of Data Engineering and AI
Chapter One
Introduction to Data Engineering and AI
What is Data Engineering?
The Role of AI in Data Engineering
The Impact of AI on Data Engineering and Analytics
Exploring the Potential of AI in Revolutionizing Data Engineering
The Role of Data Engineers in AI-driven Environments
Key Challenges and Opportunities in Data Engineering with AI
Takeaway
Chapter Two
Fundamentals of Data Engineering
Overview of Data Engineering: Concepts and Principles
Data Collection Techniques and Best Practices
Data Modelling and Schema Design
Data Validation and Cleaning
Data Integration and Transformation Strategies
Introduction to Data Warehousing and Data Lakes
Takeaway
Chapter Three44
AI-powered Data Storage and Management
Evolution of Data Storage Technologies
Introduction to Cloud Storage and Distributed Systems
AI-Powered Data Cataloging and Metadata Management
Leveraging AI for Efficient Data Storage and Retrieval
Managing Big Data: Scalability and Performance Considerations
Data Lifecycle Management and Archiving Strategies
Data Security and Privacy in AI-driven Environments
Takeaway
Part Two
Advanced Techniques in Data Engineering
Chapter Four
Data Processing and ETL with AI
Understanding ETL Processes
Enhancing ETL Pipelines with AI and Machine Learning
Streamlining Data Processing and Workflow Automation
Real-time Data Processing with AI-driven Technologies
Stream Processing Frameworks
Real-Time Predictive Analytics Using AI Models
Scalability and Fault Tolerance in Real-Time Data Processing
Performance Optimization and Error Handling Techniques
Takeaway
Chapter Five
AI-Driven Data Transformation and Feature Engineering
Significance of Feature Engineering
Automated Feature Extraction
Time Series Analysis and Feature Engineering
Dimensionality Reduction Techniques for Large Datasets
Handling Missing Data Using AI-Driven Imputation Methods
Feature Selection and Importance Estimation With AI Models
Takeaway
Chapter Six
AI-driven Data Visualization and Reporting
Importance of Data Visualization in Data Analytics
Exploring Data Visualization Techniques and Tools
Leveraging AI for Interactive and Dynamic Visualizations
Automated Report Generation with Natural Language Generation (NLG)
Designing Effective Dashboards for Actionable Insights
Takeaway
Part Three
Advanced Applications and Future Perspectives
Chapter Seven
Advanced Analytics with AI
Introduction to Advanced Analytics and Predictive Modeling
Leveraging AI for Exploratory Data Analysis (EDA)
Machine Learning Algorithms for Classification and Regression
Natural Language Processing (NLP) for Text Analytics
AI-Powered Recommendation Systems and Personalization
Text Mining and Sentiment Analysis
Visual Analytics and Data Storytelling Using AI-Driven Tools
Anomaly Detection and Outlier Analysis Using AI
Takeaway
Chapter Eight
AI-powered Data Governance and Compliance
Understanding Data Governance and Regulatory Compliance
AI-based Data Quality Management and Metadata Management
Ensuring Data Ethics and Bias Mitigation in AI-driven Environments
Data Cataloging and Data Lineage with AI
AI-driven Data Governance Frameworks and Best Practices
Takeaway
Conclusion
References
