Feature Store Open-Source Point-in-time Support Data Source Feature Transformation Feature materialization Performance Feature Type
Feathr Open-source Point-in-time. Supports various timestamp formats. Supports most major sources and file formats(csv, parquet, avro, orc, delta lake) Native transformation support with declarative framework •Row-level transformation, window aggregation transformation. •Supports offline, streaming and online transformations. Supports feature materialization via both Python API and configuration files + CLI Redis, CosmosDB, AeroSpike, SQL Scales. Performant. with built-in, low-level Spark optimizations Tensor type (for deep learning/ML) + Primitive Types
Databricks Feature Store Proprietary Only time-travel (No point-in-time support). Limited. Delta Lake tables for offline and Amazon Aurora for online. No native transformation support. •Only general data processing with PySpark notebook. •Users must know PySpark. •No online feature transformation. •Vendor locked to Spark. Manually managed by notebook Doesn’t have Spark optimizations but still scales because of Spark Primitive Types
Feast Open-source •Point-in-time and requires a fixed timestamp format. •Timestamp is always required even for non-time-series data. Supports most major sources. Doesn’t support CSV. Only row-level transformation with Pandas (Python library) Supports feature materialization via CLI Single node. In-memory. Doesn’t scale. Primitive Types