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Doug Henschen
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10 Lessons Learned By Big Data Pioneers

How can you prepare for the big data era? Consider this expert advice from IT pros who have wrestled with the thorny problems, including data growth and unconventional data.
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Database expert Richard Winter advises those planning investments in new data-warehousing platforms to consider six dimensions of scalbility: data size, data complexity, number of users, query volume, query complexity, and data latency requirements. Lots of concurrent users (like 1,000 or 10,000 or more), mixed queries, and complex analyses can be every bit as constraining as sheer big-data scale. Fail to anticipate demands along any one of these dimensions and you may outgrow your system sooner than expected.

Finally, it's an absolute must to test your prospective platform with your most complex data, your hardest queries, and a best attempt at replicating the workloads you'll face in terms of numbers of concurrent users and the mix of queries that will challenge the data warehousing platform.


Big Data A Big Backup Challenge

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IBM Picks Hadoop To Analyze Large Data Volumes

Hadoop Big Data Startup Spins Out Of Yahoo

2 Ways Big-Data Analysis Pays Off

A Model For The Big Data Era

Machines Are Driving The Big-Data Era

NOAA CIO Tackles Big Data

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