Setting Memory Limits for R in SQL Server 2016

In previous blogs, we’ve looked at the potential for integrating the statistical language R into soon-to-be-released SQL Server 2016. Developers and analysts are excited about the new possibilities – the response of administrators is considerably more restrained. Administrators are a conservative lot – it’s part of their job description. When learning of a new feature […]
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SQL Server 2016: R Integration Redux

SQL Server 2016 has now reached Release Candidate 3 (RC3). One of the new features that continues to foster interest in the analytics and data mining community is the integration of SQL Server with the open-source statistics toolset R. If you fall into this category, I’m inclined to suggest you remain patient and wait for […]
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SQL Server 2016: Choosing Your R-Chitecture

Enthusiastic data miners are excited about the new possibilities which have opened up with Microsoft R. Not everyone, however, has realized that the so-called “in-database” R is not the best choice for every application of R to SQL Server data. One database administrator expressed concern about R competing with SQL Server for precious RAM. A […]
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A Problem with R

  The value of R lies in the enormous quantity of code contributed by analysts and academic researchers over many years, providing a packaged solution not only for common analytical techniques but also the esoteric and the obscure. The problem with R, and one that concerns many analysts dealing with large data volumes, is that […]
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Querying SQL Server Data from Spark with Scala

Spark, as with virtually the entire Hadoop ecosystem, is built with Java, and of course Spark’s shell default programming language, Scala targets the Java Virtual Machine (JVM). For better or for worse, today’s systems involve data from heterogeneous sources, even sources that might at first seem an unnatural fit. Such is the case with reading […]
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