Data engineers managing high-throughput machine learning pipelines often face a bottleneck: the overhead of individual API calls. When scaling feature ingestion to thousands of records per second, the cumulative latency of network connections can degrade pipeline performance. Amazon SageMaker Feature Store is addressing this with the introduction of two new APIs designed to streamline both data ingestion and visibility.

Scaling Ingestion with BatchWriteRecord

The new `BatchWriteRecord` API allows developers to process up to 25 records in a single request. This shift from the traditional `PutRecord` model, which required an individual call for every entry, significantly reduces connection overhead. By targeting multiple feature groups within one request, teams can optimize data collection efficiency in large-scale pipelines.

This API utilizes a partial-success model. Unlike transactional APIs that roll back entirely upon a single failure, `BatchWriteRecord` processes each record independently. If specific records fail, the successful ones are still committed. Developers are encouraged to implement a retry logic that targets only the failed entries, rather than the entire batch. Conditional write policies remain intact; the system uses the `EventTime` provided in the request to ensure that only the most recent data updates the online store. If an incoming record has an older `EventTime` than the existing data, it is routed to the offline store for historical logging, ensuring the integrity of the online feature store.

Implementing Robust Retry Strategies

Because `BatchWriteRecord` returns only the failed or unprocessed items in its response object, the application layer must be responsible for handling errors. When a response contains `Errors` or `UnprocessedEntries`, developers should implement an exponential backoff strategy to prevent overwhelming the service during periods of high load or network instability. The following Python logic illustrates how to handle these partial failures:

python

Example of retry logic implementation

if response['Errors'] or response['UnprocessedEntries']:

retry_records = response['Errors'] + response['UnprocessedEntries']

Apply exponential backoff to re-send retry_records

Beyond performance, the API offers granular control over storage destinations. Developers can independently specify whether a record should be written to the OnlineStore, the OfflineStore, or both, overriding the default feature group settings if necessary. To use this feature, the calling entity must have `sagemaker:BatchWriteRecord` and `sagemaker:PutRecord` permissions, which are verified against the Amazon Resource Name (ARN) of each target feature group.

Enhancing Visibility with ListRecords

The `ListRecords` API provides a direct way to enumerate active record identifiers within a feature group. Previously, developers using the Standard tier often relied on Amazon Athena to query offline data, which introduced latency and additional costs. `ListRecords` removes this dependency, allowing for real-time visibility into the current state of the feature store.

This API works across both Standard and In-Memory tiers. It automatically filters out deleted or expired data, returning only active identifiers. For large datasets, the API supports pagination via `NextToken`. Developers can iterate through the entire set of identifiers by passing the token from one response into the next request. It is important to note that this API returns only the record identifiers; to retrieve the actual feature values, a subsequent `GetRecord` call is required. This functionality is particularly useful for compliance workflows, such as verifying data deletion or cleaning up In-Memory feature groups before they are decommissioned.

Operational Efficiency and Compliance

The integration of these APIs fundamentally changes how teams manage the data lifecycle in Amazon SageMaker Feature Store. By replacing manual query-based checks with the `ListRecords` API, teams can avoid the "ghost data" issues common in In-Memory tiers, where orphaned records could previously persist without a clear path for identification or deletion.

Combined with the reduced connection load of `BatchWriteRecord`, these tools allow for more precise control over infrastructure costs and regulatory compliance. By auditing active identifiers and selectively deleting records, engineers can maintain leaner, more efficient feature stores that align with data retention policies. As these pipelines grow, the ability to programmatically manage data at scale—rather than relying on ad-hoc queries—becomes the standard for robust ML operations.