Comparisons
The choices, side by side
Each layer of the stack has several viable options, and the arguments between them are usually narrower than the marketing suggests. Every comparison here leads with the table, then explains when each option is the right call.
Apache Iceberg compatibility matrix
Which engines and catalogs support Iceberg reads, writes, row-level deletes, deletion vectors, format v3, branches, REST catalogs, views and time travel?
Ten engines and six catalogs, feature by feature, with an official source behind every cell and the date it was last checked.
Open the matrixLakehouse vs warehouse vs data lake
What is the actual difference between a data lake, a data warehouse, and a lakehouse?
A data lake gives you cheap storage without guarantees, a warehouse gives you guarantees without engine choice, and a lakehouse adds the guarantees to the lake so you keep both.
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Which open table format should sit at the table layer?
Iceberg has the widest multi-engine support, Delta is strongest inside the Databricks ecosystem, Hudi is built around upserts and incremental reads, and Paimon targets streaming-first workloads.
Read the comparisoncatalogComparing lakehouse catalogs
Which catalog should govern the tables?
The catalog choice is mostly a governance decision: all of them can point engines at tables, but they differ sharply in how they handle permissions, credential vending, and whether their API is open enough to swap later.
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