[Future Data] Mooncake: Real-Time Apache Iceberg Without Compromise

Speaker:

Cheng Chen

System:

Mooncake

Apache Iceberg is great for large-scale analytics, but it was built for batch workloads. For streaming use cases, keeping tables fresh means writing snapshots more often, which creates excess small Parquet files, bloated metadata, and costly compaction that never ends. Updates and deletes make things worse because equality deletes push the burden to query engines, leaving readers slow and inefficient.... Read More