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Author: Kevin Feasel

Updates on the Spark Connect Dotnet Library

Published 2024-11-18 by Kevin Feasel

Ed Elliott has an update for us:

There have been quite a few changes in the last couple of months and I just wanted to give a quick update on the current state of the project. In terms of usage I am starting to hear from people using the library and submitting pr’s and requests so although usage is pretty low (which is expected from the fact that the Microsoft supported version usage wasn’t very high) it is growing which is interesting.

Read on for thoughts on production readiness, support for Spark 4.0, a couple of other updates, and some future plans.

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Comparing Numeric Data Types between MySQL and PostgreSQL

Published 2024-11-18 by Kevin Feasel

Aisha Bukar compares data types:

Numeric data types serve as a foundation in DBMS. It helps represent numbers in different forms which helps in storing and manipulating arithmetic operations on numerical data.

When dealing with numeric data types in databases like MySQL and PostgreSQL, it’s important to understand key concepts like range, precision(fixed-point numbers, floating-point numbers), and storage. This helps you understand how data is stored, processed, and retrieved.

Read on for quick descriptions of the three primary properties of numeric data types (range, precision, and storage), as well as how the two relational database platforms handle numeric data.

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Creating a Numbers Table in Power Query

Published 2024-11-18 by Kevin Feasel

Koen Verbeeck builds a tally table:

Sometimes you need a numbers table (also called tally table) when you’re working with data. It’s a table with one single integer column that contains incremental numbers (0,1,2,3,4,…). In a database, it’s fairly easy to generate using either cross-joins or a generator function (GENERATE_SERIES in T-SQL). Recently, I needed such a table in Power BI Desktop. Suppose you don’t have a database as a source (or maybe you cannot change the database), so we need to generate this in Power Query.

Read on to see how.

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Tracking Column Sizes on DAX Queries

Published 2024-11-18 by Kevin Feasel

Chris Webb busts out the calculator:

I had meant to follow up my recent post on how to find the columns touched by a DAX query by writing one on how to use this technique to find the size of these columns in memory, so you can find the total size of the columns that need to be paged into memory when a DAX query runs on a Direct Lake semantic model. Before I could do that, though, my colleague Michael Kovalsky messaged me to say that not only had he taken the query from that first post and incorporated it in Semantic Link Labs, he’d done the work to get column sizes too. All that’s left for me to do, then, is give you some simple examples of how to use it.

Click through for those simple examples, though note that this requires Microsoft Fabric.

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Concurrency Control in Oracle vs PostgreSQL

Published 2024-11-18 by Kevin Feasel

Umair Shahid continues a series on migrating from Oracle to PostgreSQL:

Transitioning from Oracle to PostgreSQL can be a transformative experience for database administrators because of the subtle differences between the two technologies. Understanding how the two handle concurrency differently is critical to managing highly concurrent workloads. 

Concurrency control is essential for maintaining data consistency when multiple users access the database simultaneously. Oracle and PostgreSQL take different approaches to concurrency control: Oracle primarily relies on locking and consistent snapshots, while PostgreSQL utilizes a Multi-Version Concurrency Control (MVCC) system.

This article provides an in-depth look at concurrency control in PostgreSQL from an Oracle perspective.

Read on for that comparison.

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Missing Data in Microsoft Fabric Real-Time Intelligence Workloads

Published 2024-11-18 by Kevin Feasel

Greg Low covers a common scenario:

But another area that I see very few handling well, is the data that is missing, rather than just the data that is present. There’s a huge difference between data that arrived, and is odd, and data that just didn’t arrive at all.

One tool that’s great at working with streams of data is the Real Time Intelligence workload for Microsoft Fabric. And it’s also great at working with data that is missing from those streams.

Greg covers some of the scenarios around missing data, though not a lot on the process to fix them.

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Using the subset() Function in R

Published 2024-11-15 by Kevin Feasel

Steven Sanderson plays duck-duck-goose with the data:

Data manipulation is a cornerstone of R programming, and selecting specific columns from data frames is one of the most common tasks analysts face. While modern tidyverse packages offer elegant solutions, Base R’s subset() function remains a powerful and efficient tool that every R programmer should master.

This comprehensive guide will walk you through everything you need to know about using subset() to manage columns in your data frames, from basic operations to advanced techniques.

Click through for a description of the function and examples of it in action.

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Tenant Switching in Microsoft Fabric

Published 2024-11-15 by Kevin Feasel

Koen Verbeeck has good news:

Praise whatever deity you believe in, because it’s finally here, a tenant switcher for Microsoft Fabric (which includes Power BI). A what? Let me explain. When you have a organization with multiple tenants in Azure (also called directories in some products like Azure Devops), or you’re a consultant like me who works with multiple clients (with each their own tenants), it’s possible that you can log into multiple tenants using the same email address. This can happen if your user account was added as an external user to another tenant.

This has been a real pain, and unfortunately, that pain still exists for Power BI Desktop.

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Hyperthreading and Warehouses

Published 2024-11-15 by Kevin Feasel

Joey D’Antoni covers the impact of hyperthreading on warehouse performance:

Database performance, especially in the cloud, is always a big concern. Traditionally, cloud storage has not performed as well as high-end on-premises storage. The introduction of faster cloud storage (in Azure Premium V2 and Ultra Disk, in AWS IO1-3) and VMs with more available storage bandwidth has improved overall storage performance. However, storage is only sometimes the complete picture.

Read on for Joey’s tests and findings.

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