About this book:

*Data Engineering for Modern Enterprises* provides a comprehensive framework for building and managing data platforms that translate raw information into strategic business value. The book begins by establishing data engineering as a pivotal discipline, shifting from traditional centralized data warehousing toward flexible, decentralized architectures like the data lakehouse and data mesh. It emphasizes that technical choices—such as selecting between ETL and ELT or batch and streaming processing—must be driven by specific business outcomes, KPIs, and codified guardrails rather than technological trends.

The core of the text explores the modern data stack, detailing the implementation of high-performance compute engines like Spark, Flink, and Trino alongside advanced storage layers such as Delta Lake and Iceberg. It advocates for a "Medallion Architecture" (Bronze, Silver, and Gold layers) to progressively refine data into high-quality "data products." A significant portion of the book is dedicated to making these systems reliable through software engineering best practices, including CI/CD, automated testing, and the use of data contracts to manage dependencies between producers and consumers.

The book also addresses the critical "last mile" of data value: scaling analytics for BI, self-service, and machine learning. It introduces feature stores as a vital component for operationalizing AI and explains how metadata management and enterprise data catalogs prevent data lakes from becoming "data swamps." By integrating governance, security, and privacy directly into engineering workflows—a concept known as "governance as code"—the author demonstrates how organizations can maintain compliance and trust without sacrificing development velocity.

Finally, the text looks beyond technology to the organizational shifts required for success. It discusses the "FinOps" model for cloud cost optimization and the cultural change management needed to transition to a data mesh. The book concludes by offering a strategic roadmap for future-proofing a data platform, urging leaders to focus on iterative development, continuous upskilling, and a product-centric mindset to ensure data remains a durable competitive advantage in a rapidly evolving landscape.

What You'll Find Inside:

• Learn to design and choose between data warehouse, data lake, and lakehouse architectures based on business outcomes and data characteristics.
• Understand the trade-offs of ETL vs. ELT approaches and apply hybrid models for efficient data ingestion and transformation.
• Build reliable data pipelines with testing, CI/CD, data contracts, and embedded governance to ensure data quality, observability, and trust.
• Scale analytics for BI, self-service, and AI/ML by leveraging feature stores, streaming processing, and data product thinking.
• Implement data mesh principles for decentralized data ownership and federated governance to enable agile, domain-driven data platforms.

Who's It For:

This book is intended for data engineers, data architects, and platform engineers responsible for designing, building, and maintaining enterprise data platforms. It provides practical guidance for modernizing data infrastructures, implementing reliable pipelines, and embedding governance to support analytics, self-service, and AI/ML initiatives. Technical leads and analytics engineers seeking to understand the strategic and operational aspects of data engineering will also benefit from its comprehensive coverage of architectures, tools, and best practices.