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Managing the Tidal Wave of Data

Source: IBM
Despite the slowing economy, data growth continues due to the digitization of infrastructures, the need to keep more copies of data for longer periods, and the rapid increase in distributed data sources. This data growth creates a wide range of management challenges. Discover solutions that can help your company maximize its storage environment and reduce costs while improving service and managing risks.


Featured publications:

Best Practices for Managing Just-in-time (JIT) Production
Source: Ziff Davis Just-in-time (JIT) manufacturing “is not procrastination, but making a commitment once the scales are tipped in the favor of certainty.” How do you keep your company from falling prey to the “deer-in-the-headlights” syndrome and suffering from decision failures? In this guide, experts share their top seven best practices for deftly managing JIT manufacturing. Read More...
Enabling Real-Time Sharing and Synchronization over the WAN
Source: Solace Systems Inc. Driven by increasing business demands and the availability of technologies like in-memory databases, change data capture software, big data storage systems, and complex event processing engines, some organizations are looking to enterprise data grids to accelerate, optimize, and scale their IT infrastructure. Managing big data scale transactional and event stream information is about more than just storing massive amounts of data; it requires the intelligent collection, filtration, sharing and exposure of information via enterprise apps connected by LANs, WANs, and cloud/grid environments. Read More...
Big Data Movement—Managing Large-Scale Information in the Constantly Connected World
Source: Solace Systems Inc. Many forces in today's world of big data are driving applications to become more real-time. Data needs to go many places, be sorted and stored in different formats, and used in a wide variety of ways. Capturing high volume data streams inside and outside datacenters can be complicated and expensive using traditional software messaging middleware on general purpose servers. In order to realize the full value of “big data” some organizations are switching to real-time message-oriented middleware appliances that excel at the high-speed distribution of large volumes of data. Read More...


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