Software Engineering Radio - the podcast for professional software developers

SE Radio 720: Martin Dilger on Understanding Eventsourcing

55 min · 13. Mai 2026
Episode SE Radio 720: Martin Dilger on Understanding Eventsourcing Cover

Beschreibung

Martin Dilger, founder and CEO of Nebuilt GmbH, speaks with host Giovanni Asproni about event sourcing -- a software architecture pattern in which, rather than storing just the current state of your data, you store a sequence of events that represents every change that has ever happened in the system. This episode starts by introducing the vocabulary around event sourcing, highlighting its relationship with event modeling, event streaming, and event storming. Martin describes some of the pros and cons of the approach, including which systems it is most suitable for. The conversation ends with guidance how to get started with event sourcing, for both greenfield and legacy systems.

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Episode SE Radio 724: Jure Leskovec on Relational Graph and Foundational Models Cover

SE Radio 724: Jure Leskovec on Relational Graph and Foundational Models

Jure Leskovec, Professor of Computer Science at Stanford University and Chief Scientist at Kumo.ai, speaks with host Sriram Panyam about relational and graph language models and their transformative impact on enterprise decision-making and predictive modeling. Jure begins by establishing the critical importance of predictive modeling across industries - from fraud detection in financial institutions to customer churn prediction, lifetime value estimation, product recommendations, and healthcare risk assessment. He notes that while AI has made remarkable advances in natural language understanding and computer vision, predictive modeling over enterprise operational data stored in relational databases has been largely left behind, still relying on 30-year-old machine learning approaches that are expensive, time-consuming, and require manual feature engineering. His proposed solution to the fundamental problem with current approaches is relational deep learning and relational transformers. The discussion explores how this approach differs from traditional graph neural networks (GNNs), which Jure pioneered and deployed successfully at Pinterest. Jure concludes with practical guidance for software engineers and data scientists interested in exploring this technology.

10. Juni 20261 h 2 min