Disclosure: I am a member of the TileDB team.
Several important concepts are being conflated here, so I'll elaborate on each separately.
S3 eventual consistency: TileDB is fully aware and designed around the eventual consistency guarantees of cloud object stores [1]. When an array is opened, only the committed writes (up to the specified timestamp if using time traveling) are seen by the reader (each write produces a timestamped "fragment", which is essentially a folder). There will never be partial reads, or corrupt reads. The array is always in a readable state with committed data. This works very similar to Iceberg's method of opening a table at a snapshot for reads [2]. I don't believe that TileDB is any more affected by stall reads than Iceberg (or Delta lake), the user must reopen or re-query a table to see data from a newer timestamp/snapshot.
Handling S3 write failures: TileDB is designed for a lock-free, multi-writer scenario. All writes produce a new timestamped fragment, which is immutable after completion of the write. TileDB performs an atomic write of an "ok" object, which signals when the fragment is complete. Any fragment which is missing the "ok" file, is ignored by the reader [3]. TileDB handles corrupt or incomplete fragments by erroring out on the read. In the future, we could offer a retry mechanism for failed reads, but the important thing is we will never return corrupt or invalid results. Incomplete fragments can happen because of S3's eventual consistency; it is possible for the atomic ok file to show up before an object in the fragment "folder".
Write serializability: TileDB's fragments are written at a (timestamp + uuid) fashion. In the event of a conflicting write at the same timestamp, the uuid provides uniqueness and guarantees that there are no errors by essentially randomly ordering the conflicting fragments. The effect in the end is similar to Iceberg's cancel and retry conflicting writes, except TileDB does not have the penalty of retrying. For Iceberg, if there are two simultaneous conflicting writes, it is effectively random which one would be accepted and which one would be retried.
ACID: TileDB does not support ACID intentionally, as it was not designed to be a transactional database. TileDB was designed with a lock-free multi-writer/multi-reader model, as our use cases up until now involved a one-off massive parallel write, and then multiple concurrent reads. Lack of transactions does not yield inconsistent data though, as stated above the read/write algorithms are specifically designed with this in mind. That said, we have recognized that there are workloads where transactions are important, and we have plans to eventually add transactions to our cloud product where we have the orchestration layer needed to manage them. It is important to keep the transactional layer modular and format-agnostic (Delta Lake is blending it with Parquet) and, therefore, we will build it on top of TileDB, not inside the storage engine.
Interoperability: Obviously nothing stops someone from writing a pandas integration for Iceberg or Delta Lake. An important part of our philosophy at TileDB is extreme integration with existing tools and frameworks, which is why we strive to support things like returning numpy arrays for the Python results and doing zero-copying everywhere possible. In other words, it is not trivial to add a tool integration, just the same as it is not trivial to just add an ACID layer.
[1] https://docs.tiledb.com/main/basic-concepts/consistency
[2] https://iceberg.apache.org/reliability/
[3] https://docs.tiledb.com/main/basic-concepts/physical-storage