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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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Better data compression saves on storage, and that's still important even as hardware costs per terabyte have declined. Column-store databases, such as HP Vertica, Infobright, ParAccel, and Sybase IQ, can achieve 30-to-1 or 40-to-1 compression while row-store databases, such as IBM DB2, Microsoft SQL Server, and MySQL, average 4-to-1 compression. That's because columnar data is consistent, containing all zip codes or all purchase order numbers, for example. Rows hold a mix of data, such as all the attributes associated with an individual customer--name, address, zip, purchase order number, and so on. The Aster Data and Oracle databases offer hybrid row/columnar features. Oracle's Hybrid Columnar Compression, for one, can crunch data at a 10-to-1 ratio.

Compression levels vary depending on the data, and keep in mind that column-store databases aren't always the best choice. If your queries call on many attributes, a row-store product may deliver better performance. Indeed, row-store databases are more commonly used for enterprise data warehouses handing a mix of queries whereas column-store databases more often power focused data marts. Column-store customers include digital-media measurement giant comScore, a Sybase IQ user since 1999, and fast-growing online network Interclick, which deployed ParAccel in 2009.


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