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Stephen Swoyer

Stephen Swoyer is a technology writer with more than 16 years of experience. His writing has focused on business intelligence, data warehousing, and analytics for almost a decade. He’s particularly intrigued by the thorny people and process problems most BI and DW vendors almost never want to acknowledge, let alone talk about. You can contact him at stephen.swoyer@gmail.com.

Articles by Stephen Swoyer

Over the last half decade, we've watched SQL purists butt heads with NoSQL upstarts, Hadoop triumphalists clash with Hadump pessimists, database geeks war with application developers, and so on. In the midst of all this warring, we've tried to fit—and, in many cases, to cram—the new into the old, the old into the new, with the result that at one time or another, we've asked the impossible of all of the components in our ever-expanding technology portfolios.

Posted February 16, 2016

The data integration status quo is predicated on a model of data-at-rest. The designated final destination for data-at-rest is (and, at least for the foreseeable future, will remain) the data warehouse (DW). Traditionally, data of a certain type was vectored to the DW from more or less predictable directions—viz., OLTP systems, or flat files— and at the more or less predictable velocities circumscribed by the limitations of the batch model. Thanks to big data, this is no longer the case.

Posted January 28, 2015

To say that big data is the sum of its volume, variety, and velocity is a lot like saying that nuclear power is simply and irreducibly a function of fission, decay, and fusion. It's to ignore the societal and economic factors that—for good or ill—ultimately determine how big data gets used. In other words, if we want to understand how big data has changed data integration, we need to consider the ways in which we're using—or in which we want to use—big data.

Posted February 21, 2014

To say that big data is the sum of its volume, variety, and velocity is a lot like saying that nuclear power is simply and irreducibly a function of fission, decay, and fusion. It's to ignore the societal and economic factors that—for good or ill—ultimately determine how big data gets used. In other words, if we want to understand how big data has changed data integration, we need to consider the ways in which we're using—or in which we want to use—big data.

Posted January 20, 2014

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