Feature spotlights

IBM Spectrum Scale RAID

Allows rebuilding of failed disks in minutes instead of hours using declustered RAID and requires less raw capacity by using erasure coding instead of replication for data protection.

Unified storage

Consolidates storage for file, object and big-data workloads into a single storage pool and allows new applications using object or HDFS interfaces to still benefit from the simplicity of file data management.

True scale out capability

Scales performance, capacity, file and object linearly with automatic load balancing and avoids network-attached storage (NAS) performance bottlenecks by leveraging a parallel file system.

Global sharing and collaboration

Shares data across geographically distributed sites using unreliable networks and caches data from multiple remote sites and gains performance similar to local data using active file management (AFM).

Hadoop connector

Hadoop and Spark applications run natively using HDFS application programming interfaces (APIs). IBM ESS enables faster, more consistent data analytics by eliminating data movement and conversion into dedicated Hadoop storage and it combines multiple data sources into a single HDFS view as needed by the business.

Policy-based tiering and compression quality of service

Tiers data automatically between various tiers of storage, including tape and cloud, based on policies and reduces total cost of ownership by eliminating the need to purchase excess high-performance storage.

Integrated & modular

Enables quick deployment of the initial solution and additional blocks of storage as demands increase. IBM ESS is designed for high-availability and balanced to maximize throughput. Choosing ESS avoids the risks and complexity of other options and provides a single source of global support for hardware and software.

HortonWorks certified

Fully certified by Hortonworks, the Elastic Storage Server all-flash solution is ideal for Hadoop, Spark and other Big Data Analytics workloads. With the ability to support file, object and HDFS, data management overhead is reduced and results are faster

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