Red Hat data analytics infrastructure solution

analytics workloads have traditionally been run in siloed vertically-integrated architectures as the need for business analytics grows many more teams of data scientists engineers and analysts are tasked with providing critical information to drive the organization forward but these traditional static analytic silos cannot handle today's accelerating demand for agility across multiple teams competition for resources on individual analytic silos grows to a point that performance is compromised in response Data Platform teams have created more analytic silos for more data engineering teams each with shadowed data sets that must constantly be synchronized leading to congestion access delays and missed deadlines today leading companies are breaking free from standalone silos onto shared data center infrastructures they seek multi-tenant workload isolation with a shared data context why are they doing that the answer is simple agility RedHat can help you break down traditional silos and move analytics workloads on to modern software-defined infrastructures that give you the agility you need at the workload layer while integrating with the power and security of one shared data store for private clouds resources for each analytics cluster can be dynamically provisioned using Red Hat OpenStack platform in the same way they are often provisioned in the public cloud or Red Hat open chef container platform can be used by data scientists and engineers to dynamically manage the lifecycle and for seamless scaling of intelligent applications the Red Hat SEF storage s3 interface helps ensure this hybrid cloud experience while providing the shared data context among teams technologies and applications enabling data scientists data engineers and data analysts to each have the compute power they need without the need to hydrate copies of data sets into each new cluster now data teams can elastically provision their own dedicated compute resources for workload isolation and use Red Hat's F storage to provide a shared context pour those workloads in traditional Hadoop spark HDFS clusters if a data scientist wants to analyze datasets that exists in two different clusters they may need to copy datasets from one cluster to the other this leads to substantial duplication costs for the multi petabyte datasets that need to be copied among many analytics clusters duplicate data set maintenance is not only time consuming and prone to error but can often result in incomplete or inaccurate insights being derived from stale data dynamic shared elastic the Red Hat data analytics infrastructure solution eliminates frustrating data analytic silos by enabling multi-tenant workload isolation with a shared data context see you can get answers faster reduce compromises and constraints and get jobs done on time to learn more follow our data analytics discussions or contact us today

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