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Month: November 2022

NT AUTHORITY\ANONYMOUS LOGON Errors and How to Fix Them

Published 2022-11-03 by Kevin Feasel

Eitan Blumin reminds me of the bad old days:

Sometimes when trying to access a linked server, you’d get an error saying “Login failed for user NT AUTHORITY\ANONYMOUS LOGON”. This happens because you’re connected using Windows authentication, and SQL Server fails to “forward” your credentials to the linked server.

This issue is often called “double-hop pass-through authentication”, also known as “Kerberos delegation“, which I’ll try to illustrate with the following diagram:

Eitan provides a helpful step-by-step guide to understanding not just how to fix the problem but also what concepts like SPNs really do.

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Refreshing Excel Data Model Tables via VBA

Published 2022-11-03 by Kevin Feasel

Chris Webb does some work in Excel:

Sometimes, when you’re analysing data, you need to be able to change variables and see what the impact is: for example you might want to see what your profit margin looks like if tax rates are set at different levels. Power BI’s what-if parameter feature can do this but it has the limitation that you can’t enter any value you like – you need to create a table containing all possible variable values in advance. The advantage the Excel Data Model/Power Pivot has over Power BI for this type of what-if analysis is that you have the Excel worksheet available, which is not only a place to display your report but which can also be used as a data source for tables in the Excel Data Model, making it easy for users to enter whatever variable they want. Up until recently, though, I assumed that if you were importing data from the worksheet into the Excel Data Model you would need to take some form of manual action, for example clicking a refresh button, to load the new data from the worksheet into the Excel Data Model when the data there changed. In this blog post I’ll show you how you can use VBA to solve this problem and build an elegant what-if analysis solution in Excel where no extra clicks are needed.

Read on for that demonstration.

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Learning to Count in R

Published 2022-11-02 by Kevin Feasel

Jerry Tuttle does the math:

You would think base R would have a count function such as count(df$Team) and count(df$Team == “NYY”) but this gives the error “could not find function ‘count’”. Base R does not have a count function. Base R has at last four ways to perform a count:

Click through to learn the different ways available to you, including those built into R itself as well as other packages like dplyr. H/T R-Bloggers.

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Installing Third-Party WHL Packages in Synapse with DEP

Published 2022-11-02 by Kevin Feasel

Sabyasachi Samaddar walks through what I consider a real difficulty:

It is really challenging when you need to install third-party .whl packages into a DEP-enabled Azure Synapse Spark Instance.

According to the documentation, https://learn.microsoft.com/en-us/azure/synapse-analytics/spark/apache-spark-azure-portal-add-librar… Installing packages from PyPI is not supported within DEP-enabled workspaces. Hence we cannot just upload the .whl packages into the workspace. We need to upload all the dependencies along with the .whl package and it will be an offline installation. Now Synapse spark clusters come with in-built packages and hence we may find some conflicts when we try to install some third-party packages.

Read on to see what you need to do.

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Lessons Learned from Index Tuning

Published 2022-11-02 by Kevin Feasel

Lee Markum has seven lessons for us:

SQL Server indexing basics are critical to query and server performance. Resources, like CPU and disk, are affected by the indexes that you have, or the ones you’re missing.

In the StackOverflow2013 database we’re going to look at Badges and users. Specifically, I want to start by seeing what badges a user has and when that user received them. Some badges, because of the type of badge it is, can be awarded more than once.

Click through for a demonstration.

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Using the T-SQL OUTPUT Clause

Published 2022-11-02 by Kevin Feasel

Chad Callihan doesn’t make two calls:

Are you familiar with the OUTPUT clause in SQL Server? It can be used with commands like INSERT, UPDATE, DELETE, and MERGE. It can also be used with setting variables in stored procedures. Using the tried and true StackOverflow2013, we’ll narrow it down today to focus on how INSERT/DELETE are typically used for logging table changes as well as an example of how to use OUTPUT with stored procedures.

For really busy transactional systems, this provides a nice boost over making an update and then selecting the new values.

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Snapshot Fact Tables in a Data Warehouse

Published 2022-11-02 by Kevin Feasel

Alex Crampton explains how snapshot fact tables work in data warehousing:

The typical fact table measures activities and is known as a transaction fact table. They support a wide variety of analytic possibilities and can be used to capture detailed information about a particular process. Certain facts cannot be studied easily using this kind of design, if at all.

This blog will outline the characteristics of a transaction fact table vs those of a snapshot fact table, and when the need for a snapshot fact table arises.

Snapshot-based fact tables aren’t ideal for data load times (especially as the table gets large) but they are useful in specific circumstances, as Alex points out.

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Difficulties around A/B Testing

Published 2022-11-01 by Kevin Feasel

John Cook asks which is clearer, 1 or 2? 3 or 4? 4 or 6?

One problem with A/B testing is that your results may depend on the order of your tests.

Suppose you’re testing three options: X, Y, and Z. Let’s say you have three market segments, equal in size, each with the following preferences.

This is known as the Condorcet paradox of voting.

John also introduces the problem of interaction effects:

Suppose you’re debating between putting a photo of a car or a truck on your web site, and you’re debating between whether the vehicle should be red or blue. You decide to use A/B testing, so you test whether customers prefer a red truck or a blue truck. They prefer the blue truck. Then you test whether customers prefer a blue truck or a blue car. They prefer the blue truck.

Maybe customers would prefer a red car best of all, but you didn’t test that option. By testing vehicle type and color separately, you didn’t learn about the interaction of vehicle type and color. 

Click through for both posts as well as some good insights.

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