A Fine Slice Of SQL Server
Garry Bargsley can’t wait to truncate:
Today, I will discuss a gotcha I ran across with SQL Server Temporal Tables. In my day-to-day environment, we do not use Temporal Tables widely. However, a few are in use, and a few are coming down the pipeline for new projects.
Click through for the gotcha, as well as how you can work around the issue.
Comments closedKevin Hill has a video for us:
If you need to find the SQL Server ErrorLog in a hurry and don’t want to spend 30 minutes drilling into every drive on the server:
Fortunately, Kevin has anticipated me and has put together the “I don’t watch videos” version.
1 CommentAlex 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.
Comments closedChad 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.
Comments closedRobert Cain has a post dedicated to the strcat() function in KQL:
The
strcatfunction has been shown in previous articles, but it’s so useful it deserves a post all of its own.As usual, the samples in this post will be run inside the LogAnalytics demo site found at https://aka.ms/LADemo. This demo site has been provided by Microsoft and can be used to learn the Kusto Query Language at no cost to you.
Read on to (re-)learn the power of string concatenation, in Kusto form.
Comments closedJohn 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.
Comments closedIt’s time to look back at the 155th blog party. I was the host this month, asking about Dynamic SQL. I got quite a few responses, which I’ve gone through and summarized below. If I’ve missed someone, please ping me.
Read on for a variety of thoughts on the topic.
Comments closedChris Webb hits the turbo button:
Do you have a a large dataset in Power BI Premium or Premium Per User? Do you have more than six tables that take a significant amount of time to refresh? If so, you may be able to speed up the performance of your dataset’s refresh by increasing the number of tables that are refreshed in parallel, using a feature that was released in August 2022 but which you may have missed.
Click through for that tip.
Comments closedMeagan Longoria explains an important part of source control repositories:
I’ve seen a few people start Azure Data Factory (ADF) projects assuming that we would have one source control repo per environment, meaning that you would attach a Git repo to Dev, and another Git repo to Test and another to Prod.
Microsoft recommends against this, saying:
Read on for the citation as well as the practical reason why we don’t want multiple repos. This is true not only for Azure Data Factory but for every development project. You have one repository with branches. Certain branches represent checkpoints where code goes out to a specific environment via use of a release tool (e.g., Azure DevOps release pipelines, GitHub actions, etc.).
Comments closedRyan Majidimehr has a short list of updates for Azure Synapse Analytics but it includes a big one:
Azure Synapse Analytics provides built-in R support for Apache Spark. As part of this, data scientists can leverage Azure Synapse Analytics notebooks to write and run their R code. This also includes support for SparkR and SparklyR, which allows users to interact with Spark using familiar Spark or R interfaces. To learn more read the official how-to Use R for Apache Spark with Azure Synapse Analytics (Preview).
That it took this long for R support was a bit weird, but I’m glad it’s there now.
Comments closed