Iceberg, Delta Lake, and Hudi now overlap on the headline features. This report shows where the differences still matter: authoritative metadata, write behavior, maintenance, streaming, and migration.
ACID transactions, schema evolution, snapshot history, hidden partitioning, compaction, and metadata pruning are now expected. Feature checklists alone produce weak decisions.
How commits are coordinated, metadata scales, files are rewritten, and conflicts are resolved affects reliability and operational effort. These differences become visible under concurrency and high change rates.
A format that fits your primary engines, catalog, governance, and operations can outperform a theoretically better format surrounded by adapters and custom maintenance.
Decide which catalog, commit protocol, and governance system owns table state. Avoid architectures where multiple systems can make incompatible writes.
Separate append-heavy analytics, high-frequency upserts, streaming ingestion, ML feature workloads, and cross-engine BI. The dominant write and maintenance pattern should lead the decision.
Test concurrency, schema change, compaction, rollback, catalog load, recovery, and cost with realistic table counts and file sizes. A benchmark on one large table is not enough.
Use read compatibility, dual publishing, or metadata translation where appropriate. Define the cutover, rollback, validation, and period of parallel operation before touching critical datasets.
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