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Day: January 10, 2024

TidyDensity 1.3.0 Released

Steven Sanderson has an update to the TidyDensity package:

The latest release of the TidyDensity R package brings some major changes and improvements that open up new possibilities for statistical analysis and data visualization. Version 1.3.0 includes breaking changes, new features, and a host of minor fixes and improvements that enhance performance and usability. Let’s dive into what’s new!

Read on for that change list and how you can get a copy of the TidyDensity R package.

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SSIS on Linux

I am not amused:

In this video, we bang our heads against the wall repeatedly with respect to SQL Server Integration Services. I spend a lot more time than I want to but we do get a mostly-functional product mostly working.

This was a frustrating video to make, but I think it was important to make it clear just what SSIS on Linux can and cannot do.

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Switching between Active Relationships in Power BI Models

Meagan Longoria solves a head-scratcher:

A couple of weeks ago, I encountered a DAX question that I had not previously considered. They had a situation where there were two paths between two tables: on direct between a fact and dimension and another that went through a different dimension and a bridge table.

Click through for several examples of when this might come up, as well as how to solve the problem.

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Updates to SQL Server Troubleshooting Stored Procedures

Erik Darling shares some updates:

I’ve been doing a lot of work on all of my free SQL Server troubleshooting stored procedures lately.

If you haven’t used them, or haven’t even heard of them, now’s a good time to talk about what they are, what they do, and some of the newer features and functionality.

Read on to see what’s new. If you haven’t used any of Erik’s procedures, I highly recommend them.

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Thinking about Scale Up-Front

Andy Brownsword shares a warning:

A point of sale system being rolled out across hundreds of physical locations. Transaction data collected each night to be batch processed into a warehouse for usual types of analysis. Our integration preference was SSIS internally. A solution was deployed in preparation.

Rolling out of the new system started with a handful of locations which steadily increased as confidence grew. On the back of this the data hitting our solution was increasing too. With a trickle of data early on there were no issues as expected. A small volume of data from a small number of stores. The process flew. We left it doing it’s thing.

Read on to see the story take a darker turn and the importance of planning for scale.

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