Think Like a Data Engineer

Modern organizations do not fail because they lack data. They fail because their data is fragmented, poorly understood, difficult to trust, and hard to turn into reliable decisions.

Think Like a Data Engineer is a practical guide to building modern data systems that are clear, scalable, governed, and ready for analytics and AI. It explains the thinking behind strong data engineering: how to design pipelines, structure data platforms, improve quality, manage metadata, protect information, and create systems that business users and technical teams can trust.

The book is written for data engineers, analytics engineers, architects, technology leaders, product teams, and business decision-makers who want to understand how modern data platforms really work. It connects technical architecture with practical delivery, showing how cloud platforms, data pipelines, governance, observability, security, and AI-ready data fit together in real enterprise environments.

Inside, readers will learn how to:

Design data pipelines that are reliable, maintainable, and scalable.
Understand the role of data lakes, warehouses, lakehouses, and cloud platforms.
Improve data quality, lineage, metadata, and governance.
Prepare data foundations for analytics, automation, and artificial intelligence.
Think about data products, platform design, and enterprise-scale delivery.
Avoid common mistakes that make data systems fragile, expensive, or hard to use.

This is not only a book about tools. It is a book about how to think like a data engineer: how to turn business problems into data architecture, how to build for change, and how to create data systems that remain useful as technology evolves.

About the author

Maria Hristova is a technology professional, author, and AI innovator with experience spanning software, data, product development, and enterprise technology. Her work focuses on the intersection of artificial intelligence, semantic technologies, data architecture, and the challenge of transforming enterprise information into knowledge that machines can understand and reason over.

With a strong interest in AI innovation and product strategy, Maria explores how organizations can move beyond traditional data access, search, and generative AI toward systems built around explicit meaning, context, provenance, trust, and explainable reasoning.

Her writing brings together practical technology experience and forward-looking research into semantic AI, knowledge architectures, intelligent systems, and the evolving relationship between enterprise data and artificial intelligence.

Maria writes for technology leaders, architects, engineers, data professionals, AI practitioners, and business decision-makers who want to understand not only what AI can do, but what must exist beneath AI for it to understand and use enterprise knowledge reliably.