AI-Driven Pipelines, Predictive Data Quality, and Intelligent Analytics at Scale

Abstract

AI-Driven Pipelines, Predictive Data Quality, and Intelligent Analytics at Scale

Objectives:
1. To explore AI-integrated data engineering architectures that enable the design, automation,
and management of intelligent data pipelines for modern enterprise environments.
2. To examine predictive data quality techniques by leveraging machine learning models for
anomaly detection, data observability, and self-healing mechanisms to ensure reliable and
trustworthy data ecosystems.
3. To demonstrate the integration of Generative AI and Large Language Models (LLMs) within
data workflows, metadata management, feature engineering, and analytics platforms to
enhance decision-making capabilities.
4. To present scalable strategies for intelligent analytics and governance, including real-time
processing, automated orchestration, predictive compliance, security, and multi-cloud data
management.

5. To provide practical insights into emerging trends and enterprise implementations of AI-
driven DataOps, collaborative human-in-the-loop systems, and autonomous optimization techniques for building future-ready data platforms.

Table of Contents

CHAPTER 1 The New Era of Data Engineering: AI-Integrated Architectures
CHAPTER 2 Core Foundations: Big Data Infrastructure in the AI Age
CHAPTER 3 Designing Intelligent Data Pipelines: Principles and Patterns
CHAPTER 4 Real-Time Stream Processing and Predictive Ingestion
CHAPTER 5 Automated Orchestration: Moving Beyond Manual Scheduling
CHAPTER 6 The Rise of Predictive Data Quality (PDQ)
CHAPTER 7 ML Models for Data Anomaly Detection
CHAPTER 8 Self-Healing Pipelines: Automated Error Remediation
CHAPTER 9 Intelligent Metadata Management and Data Catalogs
CHAPTER 10 Data Observability: Monitoring Health at Scale
CHAPTER 11 Scaling Analytics: Bridging Engineering and Insights

CHAPTER 12 Integrating LLMs and Generative AI into Data Workflows
CHAPTER 13 Advanced Feature Stores for Scalable Machine Learning
CHAPTER 14 Predictive Governance: Automating Compliance and Security
CHAPTER 15 Optimization Strategies: Cost and Latency Reduction
CHAPTER 16 Architecting for Multi-Cloud and Hybrid Data Ecosystems
CHAPTER 17 Ethics, Bias, and Trust in AI-Automated Systems
CHAPTER 18 Collaborative DataOps: Human-in-the-Loop AI
CHAPTER 19 Case Studies: Enterprise Implementations of Intelligent Pipelines
CHAPTER 20 The Future of Intelligent Data: Trends and Predictions