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Author: Kevin Feasel

Building Correlation Heatmaps in R

Steven Sanderson shows two packages for building heatmaps in R:

Data visualization is a powerful tool for understanding the relationships between variables in a dataset. One of the most common and insightful ways to visualize correlations is through heatmaps. In this blog post, we’ll dive into the world of correlation heatmaps using R, using the mtcars and iris datasets as examples. By the end of this post, you’ll be equipped to create informative correlation heatmaps on your own.

Read on to see how to build heatmaps with the corrplot and ggcorrplot packages.

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Multiple Workspaces and Microsoft Fabric Git Integration

Kevin Chant can’t stop at one:

In this post I want to cover working with Microsoft Fabric Git integration and multiple workspaces. By highlighting one method that you can use in the real-world.

I must admit that I have been very keen to test this particular way of working with Microsoft Fabric Git integration and multiple workspaces.

By the end of this post, you will know one way that you can work with Microsoft Fabric Git integration and multiple workspaces. Based on real-world working practices. Including multiple branches and pull requests.

Click through to see what Kevin has in mind usingg Azure DevOps.

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Executing Transactions in PostgreSQL

Salman Ahmed rolls it back:

Transactions, like any other database, are a key component of PostgreSQL. A transaction is a sequence of one or more database operations that are executed as a single unit of work. These operations can be queries (e.g. SELECT, INSERT, UPDATE and DELETE) that modify the database’s state.

A transaction’s main purpose is to combine multiple statements into an atomic, all-or-nothing process. It ensures that either all operations within a transaction are fully completed, or none of them are executed at all. Concurrent transactions cannot see each other’s unfinished changes. Updates from ongoing transactions remain hidden until completion, at which point all changes become visible simultaneously.

This is very similar to SQL Server, except their savepoints actually work they way they’re supposed to.

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Using IS DISTINCT FROM in SQL Server 2022

Chad Callihan is distinguished:

One feature introduced with SQL Server 2022 that I’ve recently been playing around with is IS [NOT] DISTINCT FROM. This new feature can help when it comes to dealing with NULL value comparisons.

Read on for examples. Do note that x IS NOT DISTINCT FROM y does not provide a performance benefit over its equivalent of x=y OR (x IS NULL AND y IS NULL).

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Storing Log Analytics Data in the Microsoft Fabric Lakehouse

Gilbert Quevauvilliers needs a place to store this data:

Following on in my series, in this blog post I am going to use the dataflow Gen2 in Microsoft Fabric to load the data into a lake house table.

By doing this, it will allow me to store the data in a delta lake table.

In this series I am going to show you all the steps I did to have the successful outcome I had with my client.

Click through for links to the first two parts of the series, as well as a step-by-step guide for part 3.

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Flink Streaming Use Cases for Kafka Users

Jean-Sebastien Brunner gives us some use cases:

In Part One of our “Inside Flink” blog series, we explored the critical role of stream processing and why developers are increasingly choosing Apache Flink® over other frameworks. 

In this second installment, we’ll showcase how innovative teams across every industry and size are putting stream processing into practice – from streaming data pipelines to train ML models or more timely analytics to fraud detection in finance and real-time inventory management in retail. We’ll also discuss how Flink is uniquely suited to support a wide spectrum of use cases and helps teams uncover immediate insights in their data streams and react to events in real time.

This article stays more at the “art of the possible” level rather than drilling into how we can do it.

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The Concept of Schema in Relational Databases

Adron Hall explains how different relational database management systems describe schemas:

From the viewpoint of someone familiar with the general idea of a schema, it can indeed seem unusual that databases like SQL Server, Oracle, MariaDB/MySQL, and PostgreSQL each interpret and implement schemas in slightly (or sometimes, vastly) different ways. While the core idea behind a schema as a structured container or namespace for database objects remains somewhat consistent, the exact nature, utility, and behavior of schemas vary across these systems.

Read on for an overview of these for four products, as well as what the ANSI standard indicates.

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Visualizing when Lower is Better

Alex Velez inverts a common experience:

When quickly scanning, I wonder why the direct and indirect sales teams underperformed in 2022. Mostly, they fell below the goal of 90 days, exceeding their target only three times. 

Now, pausing to think more critically about the context of this scenario, I realize I’ve misread the graph—specifically the goal line. Targets and goals are often seen as minimum thresholds, not maximum limits. But in the sales industry, the goal is to close a deal as quickly as possible. In this visual, below the goal line is actually a good thing!

This graph challenges my standard construct of targets and goals, which could lead to confusion or, worse, the wrong conclusions if I’m not careful. 

Read on for five alternative ways to display this graph and (hopefully) reduce confusion.

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Setting Table and Matrix Column Widths in Power BI

Kurt Buhler controls the horizontal, Kurt Buhler controls the vertical:

One challenge of the table and matrix visuals in Power BI is that it’s difficult to precisely and consistently set column widths. Unlike in Excel, where you can set the row and column widths in a spreadsheet, you have no option in the visual interface to control the column width property. However, it’s still possible to control it in the report metadata, which is exposed in the officially supported Power BI Projects format (.pbip) which is in preview. Notably, however, opening and modifying report metadata from this format isn’t yet supported. Despite that fact, it still works reliably, so I thought I’d demonstrate how to do this.

There are a fair number of steps involved but it all makes sense in the end.

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