Optimizing Apache Iceberg for interactive analysis is not just about getting the table format right. It is about keeping tables lean, well-organized, and ready to serve low latency queries as real workloads arrive. Without the right approach, small file storms from streaming and CDC, growing delete files, and unnecessary full table rewrites can slow dashboards and experiments while driving costs up.

In this webinar, we will discuss a practical blueprint for running fast and cost efficient Iceberg tables in production for interactive analytics across engines. We will cover usage aware optimization strategies, including targeted compaction, right sized data and manifest files, effective management of equality and position deletes, and maintenance patterns that work with streaming and CDC pipelines instead of conflicting with them. We will also discuss how lightweight, specialized engines for table maintenance and optimization can replace oversized infrastructure so you achieve consistent query performance, predictable SLAs, and lower total cost without adding complexity.

Speakers
- Yingjun Wu, Founder and CEO, RisingWave
- Yuval Yogev, Co-founder and CTO, Ryft