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Month: August 2025

Power BI Dataflow Gen1 and Connecting to SQL DB

Published 2025-08-21 by Kevin Feasel

Koen Verbeeck lays out a warning:

I’m in the progress of migrating some legacy stuff at a client, and in their Power BI environment there are still quite some Power BI dataflows Gen1. I had migrated an Azure Synapse Dedicated SQL Pool to an Azure SQL DB (much cheaper for their volume of data), and in the dev/test environment all dataflows were switched correctly to the new database.

However, in production, the dataflows only wanted to connect to the Azure SQL DB production database through a gateway. Weird, right? 

Click through for a rundown of the issue, as well as another one Koen ran into regarding Azure Data Lake Storage.

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Scheduling Copy Jobs in Microsoft Fabric

Published 2025-08-21 by Kevin Feasel

Ye Xu can run more than once:

Copy Job is the go-to solution in Microsoft Fabric Data Factory for simplified data movement. With native support for multiple delivery styles, including bulk copy, incremental copy, and change data capture (CDC) replication, Copy job offers the flexibility to handle a wide range of scenarios—all through an intuitive, easy-to-use experience.

In this update, we’re excited to announce a powerful new enhancement: multiple scheduler support. This gives you even greater control over when your data moves.

Click through for a screenshot showing how you can set up multiple schedules for a specific copy job. Based on the screenshot, it seems that there is a limit to the number of schedules you can create, though that number (20) is large enough that I couldn’t imagine it being a major impediment for most people.

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Logging in PostgreSQL

Published 2025-08-21 by Kevin Feasel

Elizabeth Christensen saves some information:

A modern-day Postgres instance creates robust and comprehensive logs for nearly every facet of database and query behavior. While Postgres logs are the go-to place for finding and debugging critical errors, they are also a key tool in application performance monitoring.

Today let’s get set up with logging for Postgres – starting with the basics of what to log, how to log what you want, and as reward for your hard work – how to use these to monitor and improve performance. The Postgres docs on logs are excellent, so please consult those for the most up to date and comprehensive configurations. This blog reads between the lines a bit beyond the docs to offer some practical advice and settings. As always, your mileage may vary.

Click through for several tips and a lot of information on the topic of logging.

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Using the DAX FILTER Function

Published 2025-08-21 by Kevin Feasel

Ben Richardson digs into a function:

If you’ve ever tried to build a measure that needed more filtering power than a basic slicer, you’ve probably hit a wall.

That’s where DAX’s FILTER function comes in.

While visual filters and slicers work great for basic scenarios:

FILTER gives you row-level control to create sophisticated calculations that respond dynamically to your business logic.

Click through for an explanation of the function, as well as several examples of how it works.

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A Primer on Join Operators

Published 2025-08-20 by Kevin Feasel

Andy Brownsword takes a peek at the three most common types of join operators, plus a bonus:

When reviewing our execution plans we’ll see joins executed using different operators. The type of operator is chosen based on the data that’s available to join and how the optimiser wants to execute it.

In this post we’ll take a look at what the operators are, when they are used, and how they work. These are the operators we’ll cover:

  • Nested Loop Joins
  • Merge Joins
  • Hash Match Joins
  • (Bonus) Adaptive Joins

Read on for a quick overview of which works best when.

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Using Dropdowns in Translytical Task Flows

Published 2025-08-20 by Kevin Feasel

Jon Vöge isn’t content with slicers:

When building Translytical Task Flows for Power BI / Fabric, you’ll quickly come head to head with a few limitations:

  • They won’t work in Embedded scenarios
  • They won’t work with PBIR and PBIP formats
  • They natively only support inputs coming from the three new slicer types: Text, Button and List Slicers (all of which are in preview at the time of writing).

Read on to see how you can partially get around the third limitation.

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Using the Tabular Object Model via Semantic Link Labs

Published 2025-08-20 by Kevin Feasel

Gilbert Quevauvilliers does a bit of connecting:

In this blog post I am going to show you how to use the powerful Semantic Link Labs library for Tabular Object Model (TOM) for semantic model manipulation.

The goal of this blog post is to give you an understanding of how to connect using TOM, then based on the documentation use one of the functions.

Don’t get me wrong the documentation is great, but when implementing it, it works a little differently and I want others to know how to use it, so it can automate and simplify some repetitive tasks.

Read on for the instructions and some of the things you can do with the Semantic Link Labs library in Microsoft Fabric.

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T-SQL Tuesday 189 Round-Up

Published 2025-08-20 by Kevin Feasel

Taiob Ali summarizes this month’s T-SQL Tuesday:

I would like to thank all the participants of T-SQL Tuesday #189. If I missed your post, it was not intentional. Please let me know, and I will add it to this list.

I am proud of this community and feel lucky to be a small part of it. I admire everyone who joined the blog party and shared their thoughts on how AI is changing our careers, as well as your thoughts on AI tools.

Click through to see the responses.

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Ordered Insert Optimization in OrioleDB

Published 2025-08-20 by Kevin Feasel

Alexander Korotkov deals with hot page issues:

When many sessions try to insert into the same B-tree leaf page, classic exclusive page locking serializes progress and wastes time on sleep/wake cycles. We’re introducing a batch page insertion path that lets the session holding the page lock insert for itself and its neighbors. The result: dramatically reduced lock waits, and big gains at high client counts (2X throughput boost starting from 64 clients in our benchmark).

Click through to see how it differs from normal PostgreSQL behavior on what is one of the more common performance problems on busy OLTP systems.

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