NetApp + Onehouse: Unifying Data From BI to AI

NetApp and Onehouse describe an integration that brings an open, Kubernetes-based lakehouse platform to NetApp StorageGRID, ONTAP, and potentially Novus AI infrastructure, aiming to let enterprises analyze and prepare data where it already resides rather than copying it into a hyperscale cloud. Their testing on StorageGRID evaluated Spark ETL, Iceberg table updates and maintenance, catalog interoperability, and interactive SQL, reporting that Lakegres completed a 1 TB TPC-DS workload about 37% faster than Presto, Quanton ran it 4.4 times faster than open-source Spark, and StorageGRID delivered higher ranged-read throughput than the compared Amazon S3 setup; the authors note that hardware and network capacity affected some comparisons. The partnership also targets unstructured data stored in ONTAP, proposing workflows that parse documents, create chunks and vector embeddings, and combine them with structured data for retrieval, analytics, and AI agents using open formats such as Iceberg, Hudi, Lance, and Vortex. Looking ahead, the companies envision the same architecture operating in datacenters, public clouds, neoclouds, and AI factories, where curated training data, model outputs, and inference traces can remain portable across independently managed storage, processing, and GPU systems.

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