SageMaker Feature Store Adds Batch Writes and Record Discovery

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☁️Read original on AWS Machine Learning Blog

#batch-writing#feature-discovery#feature-engineeringamazon-sagemaker-feature-storeamazonamazon-sagemakerfeature-store

💡Learn how two new APIs streamline SageMaker feature ingestion and record discovery.

⚡ 30-Second TL;DR

What Changed

BatchWriteRecord supports writing up to 25 records in a single API call.

Why It Matters

These APIs can reduce the number of calls required for feature ingestion and simplify record discovery workflows. Builders managing offline-to-online feature pipelines may benefit from more efficient batch operations and easier inspection of stored records.

What To Do Next

Test BatchWriteRecord in your SageMaker Feature Store ingestion pipeline and use ListRecords to validate the resulting record identifiers.

Who should care:Developers & AI Engineers

Key Points

•BatchWriteRecord supports writing up to 25 records in a single API call.
•Records can be written across multiple feature groups through BatchWriteRecord.
•ListRecords enumerates record identifiers within a specified feature group.
•The update includes code examples for integrating both APIs.

🧠 Deep Insight

Background and context from public sources — not the original article. 11 sources cited.

🔑 Enhanced Key Takeaways

•The BatchWriteRecord API provides partial failure handling, allowing individual record errors to be reported without causing the entire batch request to fail.
•Users can now configure offline stores to utilize custom names for Glue and Iceberg tables, enhancing integration with external data lakehouse architectures.
•BatchWriteRecord supports granular Time-to-Live (TTL) configurations at the request, record, and feature group levels for automated data lifecycle management.
•These capabilities were deployed globally across all AWS regions supporting SageMaker Feature Store starting July 9, 2026.
•The new APIs build upon the architectural foundation established by the modernized SageMaker SDK released in April 2026.

📊 Competitor Analysis▸ Show

Feature	AWS SageMaker Feature Store	Google Vertex AI Feature Store	Databricks Feature Store
Batch Ingestion	Supported (up to 25 records)	Supported (Bulk Import)	Supported (Delta Lake integration)
Discovery API	ListRecords available	Metadata search via Vertex AI Search	Unity Catalog integration
Storage Format	Glue/Iceberg (Customizable)	BigQuery/Cloud Storage	Delta Lake (Native)
Pricing	Pay-per-request/storage	Pay-per-node/storage	Compute-based (DBU)

🛠️ Technical Deep Dive

BatchWriteRecord API: Enables multi-record, multi-group ingestion in a single HTTP request to reduce overhead associated with individual PutRecord calls.
ListRecords API: Provides programmatic enumeration of record identifiers, enabling automated auditing and data lineage tracking within feature groups.
Iceberg Integration: Offline store configuration now supports Apache Iceberg table formats, allowing for schema evolution and time-travel queries on feature data.
Error Handling: The API response structure includes a per-record status field to identify specific failures within a batch without requiring full transaction rollbacks.

🔮 Future ImplicationsAI analysis grounded in cited sources

Reduced operational overhead for ML pipelines

The shift from single-record ingestion to batch processing significantly lowers API request costs and latency for high-throughput feature engineering workflows.

Increased adoption of Iceberg-based feature lakes

Customizable Glue and Iceberg table naming allows organizations to integrate feature stores more seamlessly into existing data governance and lakehouse frameworks.