Data Engineering for AI: Enhance data persistence strategies for optimal AI and analytical workload performance (English Edition)

Data engineering is the critical discipline of building and maintaining the systems that enable organizations to collect, store, process, and analyze vast amounts of data, especially for advanced applications like AI and ML. It is about ensuring that it is reliable, accessible, and high-quality for everyone who needs it.

This book provides a thorough exploration of the complete data lifecycle, starting with data engineering's development and its vital link to AI. It provides an overview of scalable data practices, from legacy systems to cutting-edge techniques. The reader will explore real-time data collection, secure ingestion, optimized storage, and dynamic processing techniques. The book features detailed discussions on ETL and ELT frameworks, performance tuning, and quality assurance that are complemented by real-world case studies. All these empower the data engineers to design systems that are seamless and integrate well with AI pipelines, driving innovation across diverse industries.

By the end of this book, readers will be well-equipped to design, implement, and manage scalable data engineering solutions that effectively support and drive AI initiatives within any organization.

What you will learn

● Design real-time data ingestion and processing systems.

● Implement optimized data storage solutions for AI workloads.

● Ensure data quality, compliance in dynamically changing environments.

● Build scalable data collection methods, including for AI training data.

● Apply data engineering solutions in complex, real-world AI projects.

● Conduct SQL analytics and craft insightful, AI-driven visualizations.

Who this book is for

This book is for data engineers, AI practitioners, and curious professionals with a foundational understanding of databases, programming, and ETL processes. A basic understanding of computer science concepts, cloud computing, and analytics is helpful.

Table of Contents

1. Introduction to Data Engineering in AI

2. Managing Data Collection

3. Data Ingestion in Action

4. Data Storage in Real-time

5. Data Processing Techniques and Best Practices

6. Data Integration and Interoperability

7. Ensuring Data Quality

8. Understanding Data Analytics

9. Data Visualization and Reporting

10. Operational Data Security

11. Protecting Data Privacy

12. Data Engineering Case Studies