As organizations scale their AI, analytics, and data-driven initiatives, they face a common challenge: how to make real-time streaming data readily available for analytics without building and maintaining complex ingestion pipelines.
Join StreamNative and Starburst to learn how organizations can simplify the journey from streaming data to actionable insights using an open, modern data architecture. In this webinar, we'll explore how StreamNative's Native Kafka Service and Starburst Managed Ingestion work together to seamlessly move streaming data into Apache Iceberg tables, making it immediately available for analytics, business intelligence, and AI workloads.
You'll see how teams can leverage StreamNative's managed Kafka platform and schema governance capabilities alongside Starburst's automated ingestion and analytics platform to eliminate operational complexity, accelerate time-to-insight, and build a scalable foundation for the modern lakehouse.
During this session, we'll cover:
- How to continuously ingest Kafka data into Apache Iceberg without building custom pipelines.
- Best practices for schema management and governance across streaming and analytical systems.
- How Starburst Managed Ingestion automates data ingestion and keeps Iceberg tables fully optimized via LakeOps.
- Architectures that enable real-time analytics and AI-ready data products on top of streaming data.
- How open standards such as Kafka, Iceberg, and Trino help organizations avoid vendor lock-in while scaling their data platforms.
Whether you're modernizing your data lake, building an AI-ready data platform, or looking to unlock more value from streaming data, this webinar will provide practical guidance for creating a unified architecture that bridges real-time data and analytics.
Key Takeaways
- Stream Kafka data directly into Apache Iceberg using a fully managed approach.
- Simplify lakehouse architectures by reducing custom ETL and ingestion pipelines.
- Enable real-time analytics and AI workloads on continuously updated data.
- Leverage open technologies, including Kafka, Apache Iceberg, and Trino, to build a future-proof data platform.
