Which is better for high-frequency upserts: Apache Hudi or Apache Iceberg?

Both are mature open table formats, and the honest answer depends on what "better" is optimized for — the two projects made different bets from day one.

Hudi's case: it was purpose-built at Uber specifically for high-frequency, record-level upserts and CDC ingestion. Its Merge-on-Read table type, multi-modal indexing (especially the Record-Level Index and bucket index), and now Non-Blocking Concurrency Control are all engineered around minimizing the cost of frequent point updates at write time.

Iceberg's case: it has become the broader industry standard, with adoption across essentially every major cloud provider and query engine, and has added its own merge-on-read-style row-level delete files that substantially close the update-performance gap that used to exist. For a workload that's mostly append/analytical with occasional updates, Iceberg's wider ecosystem support and engine neutrality are hard to beat.

For a workload dominated by streaming CDC and very frequent record-level mutations, Hudi's specialization still tends to give it an edge in raw upsert throughput; for broader analytical workloads with more engine diversity and less update pressure, Iceberg's ecosystem breadth is often the deciding factor. Many teams evaluate both against their actual write pattern before committing, since a real benchmark on your own workload beats either project's marketing claims.