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operational and analytics workloads

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Published By: Teradata     Published Date: Feb 26, 2013
This survey and research report discusses shifts in the data management landscape and the movement to align data with operational and analytical workloads creating the best possible unified data architecture platform. Read on to learn more.
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big data, survey report, data management, landscape, movement, align data, operational and analytics workloads, business intelligence
    
Teradata
Published By: Gigaom     Published Date: Sep 17, 2019
Today’s data volumes, combined with the credo of data-driven everything, make for an analytics landscape radically different from that of years past. Delays involved in moving and shaping operational data into separate data warehouses, data lakes and standalone BI platforms can thwart effective operational analytics. In fact, for some organizations, such segregation of infrastructure and process may not work at all. Just because OLTP (Online Transactional Processing) and OLAP (Online Analytical Processing) are distinct workloads doesn’t mean they should take place on separate platforms. Sure, dedicated server nodes may make sense to optimize the performance of operational and analytical tasks, but they need to operate on the same data, in a coordinated fashion. Modern databases – serving the needs of both business analysts and application developers – are great platforms for implementing such business-forward architectures. To learn more, join us for this free 1-hour webinar from Gig
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Gigaom
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