DEDP — About This Book

Front matter from Patterns of Data Engineering (formerly Data Engineering Design Patterns) by Simon Späti.

Key Points

The book draws on 20+ years of experience observing how data engineering constantly repackages old ideas with new terminology
Uses convergent evolution as the central lens — where distinct evolutionary paths produce identical outcomes (birds and bees both achieving flight differently)
Goal: uncover universal design patterns applicable across data engineering regardless of tooling era

Learning Outcomes

Readers gain understanding of:

Data engineering history and current state of the art
Convergent evolution and resulting design patterns
Core definitions, history, and concepts beyond industry hype
Data engineering categories, approaches, architectures, and modeling
Tools, technologies, and future directions

Audience

Intermediate-level professionals: data engineers, architects, scientists, analysts. Assumes familiarity with SQL, programming basics, databases, ETL/ELT tools, and data modeling. Recommends prior exposure to The Fundamentals of Data Engineering.

Distinguishing Approach

Rather than narrow “Design Patterns,” the work operates at broader scope — examining convergent evolutions that reveal recurring practices and implementable solutions. The title “Patterns of Data Engineering” deliberately encompasses this comprehensive perspective.

Mental Models

Convergent evolution as pattern discovery — biology metaphor applied to tech: different tools solving the same underlying problem means the problem is the pattern, not the tool
Repackaging awareness — most “new” data engineering ideas are old ideas with new branding; recognizing this saves evaluation time and sharpens vendor assessment