Start with the conditional write, because that is the part the batch call does not change. PutRecord carries an EventTime, and the record becomes the latest version in the online store only if that EventTime beats what is already stored [6]. A record that loses the comparison is not dropped; it is written as a historical version to the offline store, for feature groups that have offline storage [7]. BatchWriteRecord preserves that same comparison per entry [3]. The write work per record is therefore identical. What goes away is one request and one connection setup per record, which is where the N x M pattern was spending its tail latency [8].
The saving is worth quantifying, since AWS supplies the inputs. Its example pipeline needs 50,000 calls a second [9], which is a thorough way to discover where your account's throttling limits sit. Divide that by the 25-entry ceiling and the floor is 2,000 requests a second [10]. The full 25-fold reduction only holds if every request is packed, and a single request can span feature groups [14], so the packing is at least achievable: five groups, five records each, one call. Real arrival rates are less obliging. A window that waits to fill 25 slots adds delay to the freshest feature, which is the feature the online store exists to serve. For that 2,000 to transfer to your pipeline, records have to arrive in groups of about 25 inside whatever staleness budget your model tolerates.
The failure surface changes shape as well. Each entry succeeds or fails on its own [11]. The response returns only the failures, split between Errors, which come back with the original record attached, and UnprocessedEntries, which are retriable; anything named in neither list succeeded [12]. Silence means success, so the client has to parse the body rather than check a status code, and AWS's guidance is to retry only the failed records with exponential backoff [13].
ListRecords is the smaller API and the larger repair. The In-Memory tier is Redis-backed [4], and it had no offline store to fall back on and no Athena query to run [16]. Identifiers lost to a bug or a failed pipeline took the records with them, permanently [15]. Paginated enumeration of identifiers [4] is precisely the primitive whose absence left that tier one bug away from silent data loss.
One case the ordering description leaves open. A stale-EventTime record aimed at an In-Memory feature group has no offline store to be filed into, because the historical-version path is scoped to feature groups with offline storage and the In-Memory tier has none [19]. The published walkthrough cuts off mid-example, so this may be spelled out elsewhere in the API reference. If you are relying on historical versions to build training sets, that is a reason to keep Standard tier groups in the picture regardless.
