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Author: Kevin Feasel

From Power BI Premium Capacity to Fabric Capacity

Published 2025-04-29 by Kevin Feasel

Jon Vöge performs a migration:

So your old Power BI Premium Capacity has run/is running out, and your organization is acquiring a new Fabric Capacity to replace it.

Perhaps the organization even decided to take the chance to move the capacity region to something a little closer to home?

If you find yourself in this situation, how do you best migrate your contents of one Capacity to another?

Read on as Jon explains the migration process within a region (which is very easy) and the migration process if you need to go cross-region (which is rather cumbersome).

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SQL Server Performance Office Hours

Published 2025-04-29 by Kevin Feasel

Erik Darling is answering questions again:

My company (using SQL Server 2019 Standard) has an algorithm that keys addresses into a varchar(40) has a cross-reference database that assigns an identity property to each new value, allowing us to post the numeric in our datasets. Production has to search their generated string keys in this database’s table to get the integer key in return. We have ensured proper string data typing on lookups and have unique compressed indexes on the string values. What would your next tuning step be if this was not getting the performance you needed?

There’s a good set of questions this time, so click through for Erik’s answers.

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Temp Table Bugs in Microsoft Fabric Warehouses

Published 2025-04-29 by Kevin Feasel

Jared Westover runs into a wall:

I was excited when Microsoft announced the ability to create session-scoped temporary tables in a Fabric warehouse. However, after using Microsoft Fabric temporary tables, I quickly felt disappointed. When will they be ready for prime time, and in the meantime, what other options are available?

Click through for Jared’s experience, although it might already be fixed.

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Executing a Fabric Data Pipeline from Azure Data Factory

Published 2025-04-29 by Kevin Feasel

Koen Verbeeck leaves the confines of Microsoft Fabric:

In the blog post Call a Fabric REST API from Azure Data Factory I explained how you can call a Fabric REST API endpoint from Azure Data Factory (or Synapse if you will). Let’s go a step further and execute a Fabric Data Pipeline from an ADF pipeline, which is a common request. A Fabric capacity cannot auto-resume, so you typically have an ADF pipeline that starts the Fabric capacity. After the capacity is started, you want to kick-off your ETL pipelines in Fabric and now you can do this from ADF as well.

Click through for the process. Though do check the warnings that Koen offers around either spending extra money by remaining in synchronous execution mode, or always getting a positive result in asynchronous execution mode, regardless of whether the underlying Fabric Data Pipeline worked or not.

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Restoring Databases using Change Data Capture

Published 2025-04-28 by Kevin Feasel

Tim Radney restores a database:

I have recently had the privilege of working with multiple clients who have been taking advantage of Change Data Capture “CDC”. Change Data Capture is a feature that utilizes SQL Server Agent to log inserts, updates, and deletes occurring in a table. It makes these data changes available to be consumed in a relational format. These changes are captured for the modified rows and stored in change tables that mirror the column structure of the tracked source tables.

Change Data Capture is very often utilized for ETL projects or for data extracts into a data warehouse, data lake, etc.

Click through for the scenario and how you can keep CDC going after restoring a SQL Server database.

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Digging into the Kusto Detective Agency

Published 2025-04-28 by Kevin Feasel

Tom Zika becomes a gumshoe:

We need to answer this question:

Who is the detective that earned the most money in 2022?

We can see that only one table (DetectiveCases) was added in the ingestion section. Let’s take a look at its data.

The Kusto Detective Agency is a great way to apply KQL skills. I’m not sure it’s a fantastic experience for somebody with zero KQL knowledge, but if you’ve messed around at least a little bit with the language, this is a fun way of applying those skills.

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Incremental Backups in PostgreSQL 17

Published 2025-04-28 by Kevin Feasel

Dave Stokes takes a backup:

 The old adage that a DBA or SRE is only as good as their last backup is true.  PostgreSQL 17 added the ability to combine multiple incremental backups with a full backup to provide a complete data dictionary to recover a failed system. It is very easy to use.

This is a quick example of using incrementals. I recommend watching this video by Robert Haas for more details and some discussion of backup strategies.

Click through to see how it all works. But also please test your backups, because if I were to modify the adage, I’d say that a DBA is only as good as his last tested backup.

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Performing a Quick Filter via APPLY

Published 2025-04-28 by Kevin Feasel

Shane O’Neill tries something out:

Now, SQL Server doesn’t have the filter option, but we can do some pretty weird things, like a SELECT...WHERE statement with no FROM clause.

SELECT

    a,b,c,

    [filter?] = (SELECTb WHEREb > 11)

FROMz;

GO

It turns out that this works, but when you try to aggregate the results, it doesn’t work the way Shane expected. Using the APPLY operator does help here, so click through to see how that works.

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A Performance Comparison of Temp Tables and Table Variables

Published 2025-04-28 by Kevin Feasel

Mehdi Ghapanvari compares two ways of holding temporary data:

Do you ever wonder why your query is slow when you use a table variable in a join operation? If you are interested in why this happens and how to improve query performance in such a scenario, keep reading to learn more.

Click through for the full article. This focuses entirely on classic table variables and not memory-optimized table variables, the latter of which can significantly improve performance in specific circumstances.

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