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Big data analytics can present big problems: the larger the data set, the more expense must be allocated to storage and even then, some storage infrastructures struggle to provide the capacity and performance these workloads require.
Larger data sets necessitate large footprints of space and power for storage, and in many cases, these systems are legacy SANs with associated vendor lock-in and exorbitant support contracts.
So how can you circumvent these challenges to meet the storage needs of big data and analytics apps?
Read the following case study to discover how a leading research institution was able to double performance for demanding Spark analytics workloads using NVMe-oF and HyperParallel storage.