We invite submissions across the following themes:

🔹 Data Streaming + AI – The convergence of AI and data streaming is reshaping how organizations build intelligent, real-time systems. Share your experience using data streaming technologies to power AI-driven applications, real-time machine learning pipelines, or intelligent automation. How do you ensure trustworthy real-time data for AI assets? What are the best practices for integrating streaming data into AI models?

📖 Learning Data Streaming (Pulsar, Kafka, Ursa, or more) – Are you passionate about educating others? Submit an entry-level talk introducing Pulsar, Kafka, or Ursa, covering key concepts, architectures, and best practices to help newcomers kick-start their data streaming journey.

🔍 Deep Dive into Data Streaming – For technical experts, we invite advanced discussions on Pulsar, Kafka, Ursa Engine, and cutting-edge architectures such as leaderless streaming, lakehouse-native storage, and real-time data processing optimizations. How do you design scalable, high-performance streaming infrastructures? What challenges have you solved at scale?

🌊 Real-Time Ingestion into the Data Lakehouse – Streaming is the backbone of modern lakehouse ingestion, enabling AI and analytics teams to work with the most up-to-date and trustworthy data. We welcome talks on best practices, challenges, and architectures for integrating real-time streaming with data lakehouses like Delta Lake, Iceberg, and Hudi.

📖 Unifying Governance and Access with Data Catalogs – As real-time and AI-driven data ecosystems grow, governance and unified access become critical. We invite talks on strategies for bridging real-time streaming with data catalogs and metadata management, ensuring data consistency, security, and AI readiness.

🐿 Stream Processing and Ecosystem – Have you built tools or integrations that enhance Flink, Spark, RisingWave, or other stream processing frameworks? Share your experiences in optimizing event-driven architectures, real-time analytics, and complex streaming pipelines. How do you balance latency, scalability, and cost-efficiency in stream processing?