Amazon SageMaker Feature Store vs OpenMLDB

Amazon SageMaker Feature Store has no write-up on Specifiction yet; its figures are above.

OpenMLDB is a free, open-source machine learning database and feature platform for keeping features consistent between training and inference. It uses SQL to create feature-engineering scripts, deploy them online, and configure online data sources. Its architecture combines a real-time SQL engine, a batch SQL engine based on a tailored Spark distribution, and a unified execution-plan generator. Documentation says the real-time engine can produce features in a few milliseconds. SQL extensions include LAST JOIN and WINDOW UNION. OpenMLDB has a cluster version for large-scale production applications and a lightweight standalone version for evaluation and demonstration. Listed production capabilities include distributed storage and computing, fault recovery, high availability, scale-out, upgrades, monitoring, and heterogeneous memory support. Integrations include importing Apache Pulsar streams and adding feature-engineering tasks to DolphinScheduler workflows. Kubernetes deployment is described for both offline and online engines, but the documented cluster setup lacks a TaskManager, so LOAD DATA, SELECT INTO, and offline-related functions are unsupported there. The Spark distribution provides Scala, Java, Python, and R interfaces; its precompiled AllinOne version supports Linux and macOS.