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Traditional databases are limited in their scalability and become prohibitively costly as they near
their limits. Massively parallel processing (MPP) database systems provide an alternative to manage such data growth by using multiple servers to analyze the data in parallel. However, MPP databases often require significant server-to-server data transfer during analysis of data. Similarly, management tasks such as addition of new servers for capacity expansion and recovery from server failures also require redistribution of large data volumes over the network. These databases are inefficient in such data transfer, causing limited bandwidth of the network to become a bottleneck for overall system performance. This restricts the ability of MPP database systems to scale efficiently and exploit the full
benefits of parallelization. |
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| Register for this complimentary whitepaper to learn how Aster nCluster unlocks the full power of massive parallelism with: |
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Patent-pending
network optimizations that deliver vastly improved performance and scalability using lowcost
commodity hardware. |
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Both logical (data and queryaware)
as well as physical (data-neutral) optimizations for network efficiencies. |
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A scalable database platform that helps organizations turn the challenge of large data volumes into a strategic advantage. |
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