CaSE: Conversations about Software Engineering Data Architecture with Christoph Windheuser
17 snips
Jul 2, 2025 
Christoph Windheuser, a seasoned data expert with over 30 years in the field and a PhD in AI, shares insights on evolving data architectures. He discusses the transformative shift from data warehouses to concepts like data lakes and data mesh. Christoph emphasizes the critical importance of data quality and governance, relating them to real-world challenges in AI. The conversation touches on emerging technologies like large language models and practical applications in data management, weaving together technical depth with visionary trends.
AI Snips
Chapters
Books
Transcript
Episode notes
Early AI PhD Experience
- Christoph Windheuser shares his PhD experience with early neural networks and machine learning in 1995.
- He highlights the early limitations due to small datasets and hardware constraints.
Limits of Classic Data Warehouses
- Traditional data warehouses handle structured data and OLAP workloads but don't scale well.
- Their vertical scaling hits limits, leading to the rise of horizontal scaling with distributed systems.
Understanding Big Data
- Big data is defined not by size but by encompassing all types of data—structured, semi-structured, and unstructured.
- Handling diverse data formats requires different architectures beyond classic databases.
