"There are ten strong reasons why competitive organizations are turning to new data management solutions to handle their growing data volumes and evolving analytic needs. This new analytics platform merges data storage and data analytics into one single system to conquer the big data challenge.

 

"Big data storage is handled by a massively parallel database architecture; big data analytics is handled by an integrated analytics engine, so that analytics run fully in-database yielding ultra high performance on large data sets. The analytics engine leverages the powerful analytics framework MapReduce. The results are cost-effective, scalable data storage, ultra high performance and richer data analysis."

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Register for this complimentary checklist on big data management and
advanced in-database analytics. Major considerations include:
Cost-effective, scalable data management - what are the requirements?
Advanced analytic queries - what's meant by advanced analytics & how easy is it?
Running rich, diverse workloads - key factors for high concurrency & performance
 
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10 Considerations
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- 10 Considerations Checklist
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