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Spark speeds up adoption of big data clusters and clouds
This article is part of the Modern Infrastructure issue of July 2016, Vol. 5, No. 7
If enterprise IT has been slow to support big data analytics in production for the decade-old Hadoop, there has been a much faster ramp-up now that Spark is part of the overall package. After all, doing the same old business intelligence approach with broader, bigger data (with MapReduce) isn't exciting, but producing operational time predictive intelligence that guides and optimizes business with machine precision is a competitive must-have. With traditional business intelligence (BI), an analyst studies a lot of data and makes some hypotheses and a conclusion to form a recommendation. Using the many big data machine learning techniques supported by Spark's MLlib, a company's big data can dynamically drive operational-speed optimizations. Massive in-memory machine learning algorithms enable businesses to immediately recognize and act on inherent patterns in even big streaming data. But the commoditization of machine learning itself isn't the only new driver here. A decade ago, IT needed to stand up either a "baby" high ...
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