How-Simon-Sptis-Ideas-Strengthened-My-Journey-in-Model-Driven-Data-Engineering
How Simon Späti’s work on data engineering vaults, semantics, and modern stacks connects with my philosophy of models, metadata, and mapping.
Summary
Simon Späti’s second brain for data engineering, his writings on the evolution of the data engineer, and his curation of tools and design patterns have significantly influenced how I think about model-driven data engineering, business-friendly mapping, and metadata-driven architectures.
His work resonates strongly with my own practice of combining models, metadata, and automation to create scalable, business-friendly data platforms.
The Influence of Simon Späti
When I first encountered Simon Späti’s work, especially his Data Engineering Vault, I recognized a kindred approach:
- A network of interconnected concepts that mirrors how complex data architectures are really built.
- A focus on exploration and discovery, encouraging engineers to think holistically rather than just in code or tools.
- A “digital garden” philosophy, which mirrors my own preference for metadata-driven design and iteration.
In many ways, his work provided both validation and inspiration for my own journey. His philosophy demonstrates that data engineering is not just a technical craft but also a discipline rooted in clarity, learning, and connection.
Shared Ground: Model-Driven Thinking
One of my recurring themes in my Medium articles is Model Driven Data Engineering (MDDE). I argue that building from logical and business-oriented models, rather than starting with pipelines and code, creates clarity and long-term resilience.
Simon Späti’s writing complements this beautifully:
- His framing of data engineering as evolving from DBA + ETL developer into a software-engineering-infused role explains why models matter more than ever.
- His attention to Entity-Centric Modeling (ECM), dimensional modeling, and Inmon vs. Kimball debates mirrors the foundational conversations I’ve also reflected on in my work.
- His emphasis on the semantic layer and data contracts resonates directly with business-friendly mapping, where models are not just diagrams but agreements between business and engineering.
Business-Friendly Mapping & the Semantic Layer
Simon’s exploration of modern analytics approaches — from data mesh to semantic models — shows that business meaning cannot be an afterthought. This aligns closely with my practice of business-friendly mapping, where the translation of business logic into models and metadata is the bridge to automation.
His perspectives reinforce the point: data pipelines should not just push bits around, but should preserve meaning, lineage, and context.
Metadata-Driven Architectures
Where Simon curates tools like DuckDB, Apache Iceberg, Delta Lake, or dbt, I see direct connections with my metadata-driven flow:
- Declarative stacks → same philosophy as declarative metadata-driven modeling.
- Modern open data stack → the technology substrate that lets metadata and automation flourish.
- Design patterns → patterns like SCD2, snapshots, and lineage management that form the “lego blocks” of modern warehouses.
Together, these echo my own journey of moving from manual ETL scripts to metadata-first, automated pipelines.
Professional Development & the Role of the Data Engineer
Finally, Simon’s attention to professional growth — books, podcasts, communities — reminds us that data engineering is not just about technology. It’s about people, skills, and shared language.
At ASN Bank (formerly De Volksbank), when I worked with the CrossBreeze team, we experienced firsthand how critical it is to bring bright people together around shared models and frameworks. Simon’s curations make this knowledge accessible and provide a compass for the next generation of data engineers.
Why This Matters
The future of data engineering will not be won by toolchains alone. It will be built on shared understanding, models, and metadata — a bridge between business and engineering.
Simon Späti’s contributions are invaluable in shaping this bridge, just as my own practice of MDDE, business-friendly mapping, and metadata-driven design aims to do. Together, they form complementary voices in a broader movement toward a more sustainable, business-aligned data engineering practice.