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Author: Kevin Feasel

Using Field Parameters in Power BI for Dynamic Views

Annamarie Van Wyk demonstrates how to use field parameters to slice data in Power BI:

If you’ve ever built a Power BI report and found yourself duplicating charts for daily, weekly, monthly, or yearly views — you’re not alone. It’s one of the most common (and frustrating) dashboard challenges: “Can we see this by day? Actually, make it by week. No wait — what about monthly?”

Instead of building five versions of the same visual, you can do it all with one — thanks to Field Parameters.

Read on to see how it all works.

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Using Workspace Folders in Microsoft Fabric

Jon Vöge walks through a few folder strategies in Microsoft Fabric:

Every time I see a new Fabric Data Platform, I see a new way of using folders. Almost.

Ranging from no folders at all, to using folders to segregate item types, over folders for bronze/silver/gold layers, to even seeing setups of them being used for DEV/TEST/PROD.

I won’t claim all of these to be equally good. But the point is that there are many different approaches you may take.

Read on for a few strategies, including ones Jon would recommend avoiding.

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Choosing a Time Series Forecast Model

Ivan Palomares Carrascosa builds a matrix:

Time series data have the added complexity of temporal dependencies, seasonality, and possible non-stationarity.

Arguably, the most frequent predictive problem to address with time series data is forecasting i.e. predicting future values of a variable like temperature or stock price based on historical observations up to the present. With so many different models for time series forecasting, practitioners might sometimes find it difficult to choose the most suitable approach.

This article is designed to help, through the use of a decision matrix accompanied by explanations on when and why to employee different models depending on data characteristics and problem type.

Ivan breaks it out into two dimensions, data complexity and univariate/multivariate, and explains which types of algorithms might work best in each.

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Error Handling in Powershell with ErrorAction

Patrick Gruenauer decides to continue:

Error handling is an important part of scripting and automation, and PowerShell provides robust tools for managing errors efficiently. One of the key features for error management in PowerShell is the ErrorAction parameter. This blog post will dive into the ErrorAction parameter, explaining its usage and providing practical examples to illustrate its usage.

Read on to see the five available options and a pair of examples.

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Optimized Compaction in Microsoft Fabric Spark

Miles Cole crunches things down:

Compaction is one the most necessary but also challenging aspects of managing a Lakehouse architecture. Similar to file systems and even relational databases, unless closely managed, data will get fragmented over time, and can lead to excessive compute costs. The OPTIMIZE command exists to solve for this challenge: small files are grouped into bins targeting a specific ideal file size and then rewritten to blob storage. The result is the same data, but contained in fewer files that are larger.

However, imagine this scenario: you have a nightly OPTIMIZE job which runs to keep your tables, all under 1GB, nicely compacted. Upon inspection of the Delta table transaction log, you find that most of your data is being rewritten after every ELT cycle, leading to expensive OPTIMIZE jobs, even though you are only changing a small portion of the overall data every night. Meanwhile, as business requirements lead to more frequent Delta table updates, in between ELT cycles, it appears that jobs get slower and slower until the next scheduled OPTIMIZE job is run. Sound familiar?

Read on to see what’s new and how you can enable it in your Fabric workspace.

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Value Filter Behavior and SUMMARIZECOLUMNS in DAX

Alberto Ferrari and Marco Russo provide an introduction:

Value filter behavior controls the SUMMARIZECOLUMNS behavior that changes how filters are applied to the measure evaluation. This is mostly relevant when developers use the filter arguments in SUMMARIZECOLUMNS.

The topic is very broad, and we will just be able to scratch the surface here. However, the good news is that most developers do not need to learn the intricacies of value filter behavior. This property has three settings: Automatic, Independent, and Coalesced. The safest and most correct setting is the one introduced in 2025: Independent. Coalesced was the default setting before 2025, whereas Automatic retains Coalesced for older models, and it sets Independent for new models.

Read on to learn more about these behaviors and what they mean for your queries.

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Microsoft Fabric Spark Connector for SQL Databases

Arshad Ali makes an announcement:

Fabric Spark connector for SQL databases (Azure SQL databases, Azure SQL Managed Instances, Fabric SQL databases and SQL Server in Azure VM) in the Fabric Spark runtime is now available. This connector enables Spark developers and data scientists to access and work with data from SQL database engines using a simplified Spark API. The connector will be included as a default library within the Fabric Runtime, eliminating the need for separate installation.

This is a preview feature and works with Scala and Python code against SQL Server-ish databases in Azure (Azure SQL DB, Azure SQL Managed Instance, and virtual machines running SQL Server in Azure).

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Simulating the Monty Hall Problem in R

Jason Bryer takes us through a classic introductory problem to Bayesian statistics:

I find that when teaching statistics (and probability) it is often helpful to simulate data first in order to get an understanding of the problem. The Monty Hall problem recently came up in a class so I implemented a function to play the game.

The Monty Hall problem results from a game show, Let’s Make a Deal, hosted by Monty Hall. In this game, the player picks one of three doors. Behind one is a car, the other two are goats. After picking a door the player is shown the contents of one of the other two doors, which because the host knows the contents, is a goat. The question to the player: Do you switch your choice?

This is one of the biggest “aha!” moments in statistics, in the sense that it is not intuitively obvious and is easy to get wrong, but once you understand why it is true, it makes reasoning over time and knowledge changes easier. H/T R-Bloggers.

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Lessons Learned from Replicating BigQuery to Microsoft Fabric

Teo Lachev shares some knowledge:

A recent engagement required replicating some DW tables from Google BigQuery to a Fabric Lakehouse. We considered the Fabric mirroring feature (back then in private preview, now publicly available) and learned some lessons along the way:

1. 400 Error during replication configuration – Caused by attempting to use a read-only GBQ dataset that is linked to another GBQ dataset but the link was broken.

Read on for additional tips, including a major one around permissions.

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Postgres Migration via Logical Replication

Elizabeth Christensen makes a move:

Moving a Postgres database isn’t a small task. Typically for Postgres users this is one of the biggest projects you’ll undertake. If you’re migrating for a new Postgres major version or moving to an entirely new platform or host, you have a couple options:

Read on for those three options, when logical replication can possibly work, and the process for upgrade. It’s definitely a bit more fiddly than other options, but it’s hard to beat with respect to downtime.

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