As data architectures shift toward real-time analytics and AI, the ability to stream data directly into open Lakehouse storage has become a defining advantage. In this session, learn how StreamNative’s Classic Engine and Ursa enable high-throughput, real-time ingestion into Apache Iceberg and other open table formats—without the need for heavy connectors or redundant pipelines.
You’ll see how StreamNative’s Lakehouse-first architecture connects Pulsar topics directly to the Lakehouse, reducing duplication, simplifying governance, and making data instantly available for analytical and AI workloads.
Key topics covered include:
- How the Classic Engine + Ursa pipeline bridges streaming and Lakehouse storage seamlessly
- Strategies to eliminate silos with a single, governed data copy
- Performing streaming transformations and data quality control at ingestion
- REST Catalog integration for streamlined table governance
- Table maintenance and compaction techniques for faster queries
- Building observability and monitoring for production reliability
If you’re building a real-time, cost-efficient, and open Lakehouse architecture, this talk provides a production-proven roadmap to success.
Sai Venkatesh is a Senior Engineer at Motorq, a leading connected-car data and analytics platform, where he works at the intersection of data infrastructure, streaming platforms, and lakehouse architectures that support AI-driven analytics.With over seven years of experience, Sai currently leads the next-generation initiatives at Motorq, enabling the generation of actionable events and insights that empower real-time decision-making for major fleet companies across the U.S.
Saravanan Rajendran is a Technical Lead Engineer at Motorq, specializing in scalable, fault-tolerant distributed systems for connected-vehicle data. With over six years of experience at companies like Myntra and Motorq, he focuses on enhancing system reliability, observability, and developer productivity to enable faster, high-quality releases.
Karthikeyan K is a Technical Lead Engineer at Motorq, focused on real-time data streaming and scalable system design for high-throughput analytics. He leads efforts in building low-latency, fault-tolerant data pipelines and improving code quality to accelerate product development.
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