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The ability to consolidate diverse workloads onto a single storage platform provides investment protection by addressing current and future challenges .
This is not to say that both file and object data stores have been neglected from innovation . Fast file emerged to consistently deliver high performance within traditional architectures for small or large files , as well as sequential or random file workloads . However , with modern data requiring all of the above at the same time , its limitations are clear . Similarly , object storage underwent its own transformation . Initially built to house large amounts of non-mission critical data , fast object emerged in direct response to the rise of cloud-native applications . These apps used object as their default storage and so required higher performance levels to process the workloads .
But what if we could bring both together and unleash their benefits in tandem for enterprises ? End-users and IT leaders have asked themselves the same questions over again : if we could have multi-purpose , highperformance , low latency storage at a manageable cost from the outset , would we even have storage tiers
and different storage types ? The answer is obvious – who would want the operational complexity ? So we must consider that as technology and economics move on , there will be architectural implications .
Enter UFFO storage
These operational challenges have led to the emergence of the UFFO storage platform . Built to combine the capabilities of fast file and object stores under one roof , enterprises can now directly address their modern data requirements and power modern applications that will enable them to innovate fast and move forwards in an otherwise turbulent business environment . But what trends have driven the need for UFFO ? Let ’ s look into the five drivers a little closer .
• The growth of machine-generated data : As enterprises increasingly use data-intensive applications , they require storage platforms that can read , store and action large sets of data and provide the real-time strategic insights without the fear of high latency or downtime .
• The rise and popularity of fast-object : For realtime analytics , Machine Learning or AI applications to deliver a return on investment , their performance needs to be consistently high . Fast-object storage has rapidly grown in demand for its ability to cost-effectively serve both ML and software development workflows .
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