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Month: May 2024

Previewing the Power BI Button Slicer Visual

Published 2024-05-13 by Kevin Feasel

Reza Rad checks out a new visual:

The Button Slicer is one of the recent visuals that is very helpful in taking your report layout and visualization to the next level. Although this visual has been available for some time, many are still unfamiliar with its features. In this article and video, I’ll take you through this visual, its features, and how you can use them to have a better Power BI report layout.

Read on to see how (at least until it’s out of preview) you can get access to the visual, as well as what you can do with it.

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Monitoring and Alerting on Fabric Capacity Metrics

Published 2024-05-13 by Kevin Feasel

Ron L’Esteve wants to know what’s happening:

With Microsoft Fabric now generally available, organizations are interested in implementing this flagship Unified Data and AI Intelligence Platform for several reasons. Its native integration within the Azure stack provides seamless and secure access to widely used technologies for data integration, business intelligence, and advanced analytics. Microsoft Fabric’s storage and compute capacity is utilized by resources within this unified analytics platform, including storage repositories, such as data warehouses and data lakes, and compute capacity for Power BI, Pipelines, DW processing, and artificial intelligence (AI)/machine learning (ML) workloads.

Fabric capacity can be purchased on Azure with a pay-as-you-go model, and a 60-day free trial (64 CUs) is offered to test the platform. Organizations that have an existing Power BI Premium capacity can easily enable access to Fabric by using the Microsoft Fabric admin switch. Enabling Fabric in Power BI Premium as opposed to Azure Portal creates a problem: there is no easy way to monitor and set alerts on your Fabric capacity metrics in the Azure Portal.

Click through to learn how to install and use the Microsoft Fabric Capacity Metrics App.

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Tips for Query Tuning in Postgres

Published 2024-05-13 by Kevin Feasel

Gabrielle Roth shares some advice:

For PGSQL Phriday #016, Ryan’s asked us to “…discuss your process for tuning difficult queries. Specifically, try to focus on that one problematic query that really challenged you and you always use it as an example when helping or teaching others your methods.”

Here are the generic steps I take, mostly in order.

Click through for those tips, as well as an example of using join_collapse_limit in practice to tame an unruly query.

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Dynamic Unpivoting of Columns in T-SQL

Published 2024-05-13 by Kevin Feasel

Kristyna Ferris does a bit of twisting:

Picture this, your data ingestion team has created a table that has the sales for each month year split into different columns. At first glance, you may think “what’s the big deal? Should be pretty easy, right? All I need to do is unpivot these columns in Power BI and I’m good to go.” So you go that route, and the report works for one month. Next month, you get an urgent email from your stakeholders saying they can’t see this month’s numbers. That’s when you realize that this table will grow with new columns every month. That means that any report you make needs a schema refresh every single month. Unfortunately, Power BI will not grab new columns from a table once it’s published into the online service. The only way for the Power Query to pivot the new columns is for you to open the report in your desktop, go to Power Query, and refresh the preview to get all the columns in that table.

Which is quite the pain. But Kristyna has a solution using the UNPIVOT operator in T-SQL.

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partialBatch Commit Mode in Power BI API

Published 2024-05-13 by Kevin Feasel

Chris Webb provides an explanation:

I have always wondered what the partialBatch option for the commitMode parameter in the Enhanced Refresh API does exactly. There is some documentation here and here but I was curious to find out more as part of the research I’m doing for my ongoing series on Power BI refresh memory errors, in case it was useful for reducing overall memory usage (spoiler: it may be). In this post I’ll share what I found out after running some tests.

Read on for the demonstration and explanation, as well as tips on when you might want to use it.

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Collapsing or Concatenating Text in R

Published 2024-05-10 by Kevin Feasel

Steven Sanderson builds a list:

When working with data frames in R, you may often encounter scenarios where you need to collapse or concatenate text values based on groups within your dataset. This could involve combining text from multiple rows into a single row per group, which can be useful for summarizing data or preparing it for further analysis. In this post, we’ll explore how to achieve this task using different methods in R—specifically using base R, the dplyr package, and the data.table package.

This is the R equivalent of T-SQL’s STRING_AGG() function, or the STUFF() + FOR XML PATH approach if you’re still on an older version of SQL Server.

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Quick Takes on Logistic Regression

Published 2024-05-10 by Kevin Feasel

John Cook talks about my favorite form of regression that serves to solve classification problems:

Logistic regression models the probability of a yes/no event occurring. It gives you more information than a model that simply tries to classify yeses and nos. I advised a client to move from an uninterpretable classification method to logistic regression and they were so excited about the result that they filed a patent on it.

It’s too late to patent logistic regression, but they filed a patent on the application of logistic regression to their domain. I don’t know whether the patent was ever granted.

Read on for a few more thoughts on and around logistic regression and logits from a mathematician.

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Arbitrary Intervals for Partitioning in Postgres

Published 2024-05-10 by Kevin Feasel

Keith Fiske does a bit of interval math:

Whether you are managing a large table or setting up automatic archiving, time based partitioning in Postgres is incredibly powerful. pg_partman’s newest versions support a huge variety of custom time internals. Marco just published a post on using pg_partman with our new database product for doing analytics with Postgres, Crunchy Bridge for Analytics. So I thought this would be a great time to review the basic and complex options for the time based partitioning.

Read on for a note of how pg_partman works and interval management, especially for versions earlier than 5.0.

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Indexing for Read-Scale Databases

Published 2024-05-10 by Kevin Feasel

Jose Manuel Jurado Diaz shares a customer case:

Today, I worked on a service request that our customer has a Business Critical database with 4 vCores and Read-Scale Out enabled. Our customer noticed several performance issues using Read-Scale Out database and I would like to explain several lessons learned found during the troubleshooting steps.

Click through for notes on troubleshooting and improving performance.

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