A Fine Slice Of SQL Server
Robert Cain continues a series on KQL:
As you’ve seen with the
joinin my Fun With KQL – Join post it can be useful to combine two tables in order to clarify the output. What if, though, you need data that isn’t in an existing table?That’s where the
datatableoperator comes into use. Thedatatableallows you to create a table of data right within the query. We’ll see a few useful examples in this post.
Read on to see how you can create one and what you can do with it.
Comments closedMarco Russo and Alberto Ferrari retrieve security roles:
Security roles automatically restrict the data visible in a Tabular model. However, for a more personalized user experience, report authors may desire to further customize elements of the report. Examples may include changing the colors of visuals or adding and modifying report information, all based on the active security roles.
This article explores how to display active security roles in card visuals. We will also outline how to implement a measure to determine if the current user belongs to a specific security role, and provide more tools for customizing the user experience based on active security roles.
Click through for the article.
Comments closedReitse Eskens looks at an oddity:
I was teaching a class and during an interesting discussing an attendee told me that views with a filter took a long time to produce results, even if the result set itself was quite small. I wanted to test this out for myself to see what was happening. I’ll take you along this short journey in this blog. The outcomes have been validated against a SQL 2017, SQL 2019 and SQL 2022 instance.
Click through for an example.
Comments closedThe security and integrity of your company’s data are crucial in today’s data-driven environment. You must have a reliable backup plan in place to protect your PostgreSQL databases against unplanned calamities. In this article, we’ll examine how to create physical backups using the PostgreSQL tool pg_basebackup. We’ll talk about client needs, business use cases, backup space complexity, disaster recovery, point-in-time recovery (PITR), and how to use PostgreSQL to put these strategies into practice.
Read on for the instructions. Of particular importance is that point-in-time recovery.
Comments closedThere was a question that came up on the MCM mailing list about what are these files: model_msdbdata.mdf, model_msdblog.ldf, model_replicatedmaster.mdf, model_replicatedmaster.ldf? It was pointed out that the files don’t show up in the typical DMVs and seem to not really be used at the moment.
These are the databases used as the basis for the system databases in contained availability groups. Definitely don’t touch them, just leave them be, even if you aren’t using availability groups (let alone contained).
Click through for more information on how you can find them and another warning not to mess with them.
Comments closedEitan Blumin spots a wild Latch Convoy:
The “Last Page Insert Contention” in SQL Server, also known as “Latch Convoy Problem“, also known as “PageLatchEx Contention” is one of those extremely rare use cases that are very difficult to see in real-world scenarios.
Evidently, it was impactful enough that Microsoft implemented a solution for this problem back in SQL Server 2019 in the form of the new OPTIMIZE_FOR_SEQUENTIAL_KEY index option, which reportedly fixes it.
Click through to learn more about a scenario in which Eitan saw this in the wild. In fairness, I’m not sure I’d do any better at realizing that this was a last page insert contention problem.
Comments closedChad Callihan enables a trace flag:
In SQL Server 2016 and 2017, trace flag 460 can be used to gather additional details about string truncation errors. You may be familiar with the “String or binary data would be truncated” error message. Have you been left wondering what would be truncated? This is where trace flag 460 comes in. When enabled, the error message will include details on where exactly the potential truncation is taking place.
Read on to see if this is something you might benefit from enabling.
Comments closedBeginner’s Hadoop gives us an overview:
The Databricks Unity Catalog is a feature provided by Databricks Unified Data Analytics Platform that allows you to organize and manage metadata about your data assets, such as tables, databases, and views. It provides a centralized metadata repository that enables users to discover, understand, and collaborate on data assets within a Databricks environment. The Unity Catalog integrates with various data sources and supports different metadata management capabilities.
Read on for an overview of what it does.
Comments closedDid you know Azure Data Factory (ADF) will actually list available properties? It will. One of the things I cover in my ADF training titled Master the Fundamentals of Azure Data Factory is this handy troubleshooting tip.
Read on to see how, though I’d personally like something which is a bit faster than waiting for the thing to execute and getting back what my choices are.
Comments closedChris Webb gives us the beginnings of an origin story:
For any Power BI person, Direct Lake mode is the killer feature of Fabric. Import mode report performance (or near enough) direct on data from the lake, with none of the waiting around for data to refresh! It seems too good to be true. How can it be possible?
The full answer, going into detail about how data from Delta tables is transcoded to Power BI’s in-memory format, is too long for one blog post. But in part it is possible through something that existed before Fabric but which didn’t gain much attention: on-demand loading.
Click through for another blog post on the topic and an idea of how these tie together.
Comments closed