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NetApp and Google Cloud Officially Launch Integrated Storage Service

Google 우선 소스Published2026.06.04 15:59
Support for integrated file and block storage operations, and multi-cloud data migration capabilities also unveiled
In the enterprise cloud storage market, movements to operate file and block data as a single service are taking shape. NetApp and Google Cloud have officially launched an integrated storage service based on Google Cloud NetApp Volumes and introduced multi-cloud data migration capabilities.

NetApp announced on the 4th that it is expanding its collaboration with Google Cloud and strengthening enterprise data management and storage capabilities. The two companies unveiled the Google Cloud NetApp Volumes Flex Unified service and NetApp Data Migrator (NDM) at the recent Google Cloud Next 2026.

Flex Unified is a service that enables the simultaneous operation of file and block workloads within a single storage pool. Enterprises can run high-performance applications, such as databases, High Performance Computing (HPC), Electronic Design Automation (EDA), and VMware, on the Google Cloud without building separate storage environments or making significant changes to existing applications.

This service is provided based on NetApp ONTAP and supports NFS, SMB, iSCSI, NVMe/TCP, etc. in a single service. Available across all Google Cloud regions, it reduces the burden of regional infrastructure operations and allows for the selective use of file and block storage based on workload characteristics.

NDM, unveiled alongside it, is a service that simplifies data migration between on-premises and multi-cloud environments. It is designed to automate complex migration tasks, enabling enterprises to transfer data without the need for specialized personnel. This focuses on reducing the burden on companies that need to redeploy data for AI analytics or cloud-based application operations.

This announcement aligns with the trend of cloud infrastructure competition expanding from computing resources to data accessibility and operational efficiency. Since AI workloads require the rapid reading and movement of large amounts of data, a distributed storage structure can lead to increased costs and latency.

The industry views this collaboration as significant in that it expands options for migrating AI and high-performance workloads to the cloud without completely redesigning existing systems. As the use of AI by enterprises expands, the importance of the data storage, movement, and management layers is also expected to grow.
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