As the boundaries between operational and analytical systems blur, schema management has become a critical foundation for reliable, scalable streaming data architectures. In this talk, we explore how defining and governing schemas outside of services can bring structure, trust, and predictability to modern data pipelines — transforming traditional data streams into streaming data products that power real-time analytics and AI applications.
Key topics include:
- Why schema management is essential in the era of Operational–Analytical integration
- How to define schemas outside your services for consistency and scalability
- Using GitOps to synchronize schema repositories with your environments
- Leveraging semantic validation with Google CEL to reduce overhead and errors
- How to evolve “boring” data streams into rich, discoverable data products
- Enabling bottom-up adoption without requiring top-down mandates
- How strong schema foundations improve Agentic AI and real-time applications
If you’re building real-time pipelines, data products, or AI-driven systems, this session offers a practical blueprint for bringing order, automation, and intelligence to your data streams.
Jan Siekierski is a Data Streaming consultant at Kentra with a background in Java and Kotlin JVM applications. He has five years of experience leading teams building event-driven systems and now focuses on advancing the data streaming industry through content creation, training workshops, and advisory consulting.
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