Randomly Sampling Rows in R

It’s impossible to imagine a data scientist who does not have to randomly sample datasets on a regular basis. Most employ the useful and easy function sample( ), defined in R’s base namespace. Let’s take a closer look at sample( ) and then take a look at a flexible alternative that is just as easy […]
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New Improved SQL Server 2019! Now with more Spark!

If your server racks are anything like mine, they look impressive and powerful, clean and efficient. From the front, that is. Look behind the rack and there is a tangled rat’s nest of power cables, network connections, and those big clumsy things connecting to KVM switches. It’s worse on the inside. Data platforms and software […]
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Power BI: How to Save Memory for Better Performance

The tabular mode data storage used by Power BI, Power Pivot, and the SQL Server Analysis Services thrives on sufficient memory. Take away memory and performance plummets. You may be wasting memory and thus performance if you do not take advantage of opportunities to trim excess fat when you are first importing your data. And, […]
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Turning Your AzureML Experiment into a Web Service

Great! You’ve created a promising machine learning model using AzureML Studio. How do you make it available to colleagues for practical use? In Microsoft-speak, how do you “operationalize” it? The AzureML Studio makes it very easy to convert an existing experiment into an Azure web service that can be accessed from any application that supports […]
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Batch Mode Processing for Row Data in SQL Server 2019

Batch mode processing was introduced in SQL Server in 2012, the same time as columnstore indexes. The combination of the two prevented SQL Server from needlessly processing column values not needed by a query, as it must do in classic row processing. This processing mode also permits SQL Server to take better advantage of cache […]
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