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Day: September 11, 2024

Query Start Times in Query Store

Hugo Kornelis describes an issue:

I was hired by a customer who had a very annoying issue with the daily data load of their data warehouse. The volume of data to be loaded is high and they were already struggling to finish the load before business opens. But that was not their biggest issue. The biggest problem, the real pain point that they hired me for, is that at unpredictable moments, the load would run much longer than normal, pushing it well into business hours. They wanted me to find out what caused those irregular delays, and find a way to stop them from happening.

Read on to learn more about the issue itself, as well as a discrepancy in what Query Store showed. Hugo also points out that the quick-and-easy solution may not be the right solution.

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Building a Test Data Generator for PostgreSQL

Mika Sutinen builds some data:

I recently had a project where I needed quickly to generate some realistic looking test data to PostgreSQL database. While I often like to go for ready-made solutions, this felt like a good opportunity to stretch my coding muscles and develop it myself. Moreover, this seemed like a fun puzzle to solve, and I could probably use the same solution later on elsewhere.

Click through for a description of the generator, as well as a link to Mika’s GitHub repo. Taking a quick peek at it, it does appear that you could probably use this for other data platforms like SQL Server with very limited modification.

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Myths and Reality of Copilot for Power BI

Kurt Buhler puts together an essay:

However, recent months reveal rising skepticism, concern and possibly even disillusionment with generative AI tools, both from investors (especially from investors) and from the public. Despite the massive investment, enthusiasm, and promotion, these tools seem to be seeing limited adoption and aren’t yet showing the measurable value that fulfills their promises. And yet, paradoxically, many professionals will agree anecdotally that they use generative AI tools regularly, and that these tools seem to help them be more productive in certain tasks. Furthermore, there are concrete success stories where generative AI is bringing value, such as the models like the latest versions of Alphafold (from Google) and ESMfold (from Meta) that aid in protein folding for pharmaceutical companies more effectively find potential new drug candidates. So, who are these tools for, what problems do they solve, and how can we use them effectively? This is too big of a topic for even Bink and Bonk the Data Goblins to solve, so let’s narrow the focus, a bit.

This is a must-read, and Kurt even provides a de-goblinified PDF version for management.

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Context Transition in DAX

Marco Russo and Alberto Ferrari draw on a cocktail napkin:

In previous articles, we introduced a visual approach to describing two important DAX concepts: the filter context and the row context. This article completes this short series by describing the context transition using a graphical visualization.

This article provides a different perspective on the context transition already covered in other articles: you should read them to get more insights on this important concept for DAX.

Read on to see how it all fits together.

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Reading Always Encrypted Data in Power BI

Rod Edwards wants to make use of encrypted data:

This is where things start to get a little more interesting compared to Pt1, as now we have a different application in the mix for reading the data. So how can that application retrieve the key needed to successfully decrypt?

Read on to see how it all works. There are a lot of working parts here, though some of it pertains to using an on-premises gateway versus Always Encrypted as such, so you get even more bang for your buck.

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