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Curated SQL Posts

Designing a Microsoft Fabric Workspace

Published 2025-04-22 by Kevin Feasel

Ron L’Esteve lays it out:

When planning a Data Platform for your organization on Microsoft Fabric, you need to consider workspaces during your design process. Proper workspace design is critical for the organization and consumption of Fabric items. Understanding how to effectively manage Microsoft Fabric workspaces can streamline your processes.

If your workspaces are not set up in a way that aligns with functional business verticals or environments (such as Dev/UAT/Prod), you will end up spending a significant amount of time and effort re-factoring this technical debt to meet the desired organizational structures. While it seems trivial to simply move a report, pipeline, or other workload from one workspace to another, the inbuilt dependencies can often be complex. With efficient planning and design efforts, these problems can be avoided.

Click through for Ron’s advice.

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Spring Cleaning for Lakehouse Tables with VACUUM

Published 2025-04-21 by Kevin Feasel

Chen Hirsh says it’s time to do a bit of cleanup:

Delta tables create new files for every change made to the table (insert, update, delete). You can use the old files to “time travel” – to query or restore older versions of your table. This is a wonderful feature, but over time, these files accumulate in your storage and will increase your storage costs.

Read on for a primer of the VACUUM command, how frequently you might want to run the command, and how much data you want to save. This example is specifically around using Databricks, but the mechanisms work exactly the same for other lakehouses like Microsoft Fabric.

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Query Memory Utilization of Distinct Count Measures in Power BI

Published 2025-04-21 by Kevin Feasel

Chris Webb does the math:

The series of blog posts I wrote last year on semantic model memory usage, in particular this post on the query memory limit and the “This query uses more memory than the configured limit” error in Power BI, gets a lot of traffic. Since writing that post on the query memory limit I’ve written a few follow-ups on common mistakes that lead to increased query memory usage, such as this one on measures that never return a blank. Today’s post is sort of in that series but it isn’t about a design mistake – it’s just to point out that distinct count measures can be surprisingly memory-hungry.

Read on for Chris’s findings and the explanation, as well as a couple of potential workarounds if you find yourself in this situation.

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Building a Microsoft Fabric Notification Bot for Teams

Published 2025-04-21 by Kevin Feasel

Stepan Resl gets the message:

Power BI provided a wide range of notification options, each with its own advantages and disadvantages, often requiring us to combine them to achieve the desired result. Historically, we primarily relied on Subscriptions, Alerts, Outages Emails, and Refresh Error Emails. Combining these options was complex and inefficient. With the introduction of the ExecuteQuery endpoint in the Power BI REST API, entirely new possibilities opened up. By sending a DAX query to a selected dataset and processing the returned data, we can create virtually any custom notification, enabling data-driven notifications. This endpoint also came with built-in support for Microsoft Power Automate and Logic Apps, significantly simplifying the process due to their native connectors to other services like Exchange, SharePoint, and Microsoft Teams (including support for Adaptive Cards).

Read on for a bit of history, why a custom solution was necessary for Stepan, and the process of building out that solution.

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ReplaceInName in dbatools’ Backup-DbaDatabase

Published 2025-04-21 by Kevin Feasel

Jess Pomfret discovers a new flag:

Recently I was reading the docs for `Backup-DbaDatabase` and found a parameter I didn’t realise existed, but is so useful when you want to automate backups, but keep control of the file names.

Click through to learn more about the specific feature, as well as a reminder that it’s a good idea to read through the documentation. Not all documentation is good, but the work people have put into dbatools means that there is often a good example involving most of the available parameters. It turns out that there are, in fact, two examples that use -ReplaceInName in the documentation for Backup-DbaDatabase, so you get not only a description of the parameter but also two specific examples of how to use it.

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Troubleshooting a Slow Mapping Data Flow in Azure Synapse Analytics

Published 2025-04-21 by Kevin Feasel

Reitse Eskens has the need for speed:

The issue was quite straightforward. The client has a mapping data flow in Synapse that processes a few hundred to a few thousand rows but takes 15 minutes to complete. The low number of rows compared to the time necessary is a cause for concern.

The data extraction needs a staging storage account where the data is written into TXT files. The second step of the mapping data flow reads the TXT files and writes them out in delta format, which is Parquet files.

The source is an S4Hana CDC table, the target of which is a regular Azure storage account.

Read on for Reitse’s summarization of the troubleshooting and testing process, as well as what ended up working for this customer.

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Creating a Dashboard in Snowflake

Published 2025-04-18 by Kevin Feasel

Kevin Wilkie doesn’t just do e-mails:

If you haven’t had a chance to mess around in Snowflake, there are internal dashboards that you can provide to your users (or just use yourself) so that you can get the latest data.

Today, I want to quickly show a use case for these Dashboards and one way that you can get data quickly and easily in a nice format.

And right above Dashboards is Streamlit, one of my favorite libraries for building rapid application prototypes and even small data applications.

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Comparing Apache Iceberg to Delta Lake

Published 2025-04-18 by Kevin Feasel

Maria Zakourdaev compares technologies:

Public cloud blob storage has been a standard for data lakes for the last 10 years. Blob storage, at first, came to solve data warehouse storage limitations. It is very cheap and has unlimited capacity. You can store any data format (structured, semi-structured, or unstructured) in the data lake located on a blob storage, and keep any amount of raw data for an unlimited time. When considering Apache Iceberg vs Delta Lake, both can manage data efficiently. Depending on the access frequency, data can be stored on cold or warm types of cloud storage, saving even more costs.

Read on to see how the two techniques compare along several dimensions, as well as some general guidance at the end on which to choose.

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Troubleshooting an Apache Flink Job Not Producing Results

Published 2025-04-18 by Kevin Feasel

Wade Waldron digs in:

Imagine that you have built an Apache Flink® job. It collects records from Apache Kafka®, performs a time-based aggregation on those records, and emits a new record to a different topic. With your excitement high, you run the job for the first time, and are disappointed to discover that nothing happens. You check the input topic and see the data flowing, but when you look at the output topic, it’s empty.

In many cases, this is an indication that there is a problem with watermarks. But what is a watermark?

Read on for a primer on watermarks, followed by an explanation of the common solution to the problem Wade describes.

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