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Month: June 2024

Three Partitioning Options in Postgres

Published 2024-06-13 by Kevin Feasel

Semab Tariq shows how to perform three types of partitioning in PostgreSQL:

PostgreSQL is renowned for its exceptional performance in managing data. One of its standout features is partitioning, a technique that divides large datasets into smaller, more manageable segments. Partitioning provides several benefits, including improved query performance, streamlined data management, and enhanced scalability. By organizing data into partitions, PostgreSQL can execute searches more efficiently and handle tasks with greater ease. 

In this blog, we will delve into the details of partitioning in PostgreSQL, exploring its various types, advantages, and drawbacks. We’ll uncover how partitioning can revolutionize data management and decision-making processes in database environments.

Click through for demonstrations of range, list, and hash partitioning.

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Build a Custom Semantic Model for Microsoft Fabric

Published 2024-06-13 by Kevin Feasel

Reza Rad offers up some advice:

The Lakehouse or Warehouse comes with a default Power BI Sematic model, which can be used for reporting and analytics. However, you can also build and use a customized semantic model. There are significant differences when using the semantic model in real-world analytics projects. In this article, I’ll explain the difference between these two, which one is recommended, and why.

Click through for the video, as well as the article.

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New Video: The Naive Bayes Set of Algorithms

Published 2024-06-12 by Kevin Feasel

I have a new video:

In this video, I cover a class of algorithm that is neither particularly naive nor particularly Bayesian: Naive Bayes.

I am a bit tongue in cheek with that description, as technically I’ll give you that the class of algorithms is “naive.” But I do still have some fun with the name and then show how we can use Naive Bayes to build a quick-and-dirty model that’s at least somewhat effective.

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Orphaned Users in SQL Server

Published 2024-06-12 by Kevin Feasel

David Seis puts the orphans to work:

In SQL Server, a user becomes ‘orphaned’ when it exists within a database but lacks an associated login at the server level. This typically occurs when a database is either moved or restored to a different SQL Server instance. To understand why, it’s important to note that while logins are created at the server level, users are created at the database level. Each login is linked to a unique Security Identifier (SID). Therefore, during the process of moving or restoring a database, the SIDs may not align correctly, resulting in orphaned users.

Read on for a script to find and fix orphaned users.

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Microsoft Fabric: Lakehouse or Warehouse?

Published 2024-06-12 by Kevin Feasel

Koen Verbeeck helps us choose:

This doesn’t mean no code has to be written. On the contrary, in this article we’re going to focus on two services of Fabric: the lakehouse and the warehouse. The first one is part of the Data Engineering experience in Fabric, while the latter is part of the Data Warehousing experience. Both require code to be written to create any sort of artefact. In the warehouse we can use T-SQL to create tables, load data into them and do any kind of transformation. In the lakehouse, we use notebooks to work with data, typically in languages such as PySpark or Spark SQL.

Read on for the comparison. I tend to go more for the lakehouse experience rather than warehouse, but Koen provides a lot of the info you’d need in order to make the right decision for yourself.

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A TDS Security Non-Issue

Published 2024-06-12 by Kevin Feasel

Sean Gallardy is not amused:

Security must be making the rounds again as I’ve recently had quite a few inquiries about a “vulnerability” in SQL Server connections. I am in the camp of, if it isn’t easy to exploit or is completely theoretical and the exploit is barely an inconvenience such as, “could allow a local admin user to rearrange the startup order”, I don’t get very excited. Enter in the old-fad-turned-new “vulnerability” of version information. Yes, you didn’t misread that. Version. Information. At the very worst it’s potentially information disclosure and even that is a stretch.

Read on for an eminently sensible take.

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Stop and Start Fabric via Power Automate

Published 2024-06-12 by Kevin Feasel

Gilbert Quevauvilliers saves some money:

Stop and start your Fabric Capacity using Power Automate

With Fabric Capacities trial coming to an end, you need to make sure to stop and start Fabric Capacities.

In my blog post below, I am going to show you how I can start or stop my Fabric Capacity by simply sending an email to myself with the details in the Subject Line to start or stop the capacity.

That’s a pretty neat method, especially if you have odd hours you want to run the capacity.

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Dataverse and Microsoft Fabric Gotchas

Published 2024-06-12 by Kevin Feasel

Marc Lelijveld shares some advice:

Recently, I architected a solution for a client for their Microsoft Fabric data platform. The client works with Dynamics Finance & Operations as one of their main ERP system. Fabric offers easy ways to bring data from various standard Microsoft services into the platform, however it is not always as easy as it looks like. In this blog I will elaborate on the gotcha’s encountered in architecting this solution.

Read on for the challenges that Marc ran into along the way.

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Choosing between Data Warehouses, Lakes, and Lakehouses

Published 2024-06-11 by Kevin Feasel

Den Smyrnov talks architecture:

Historically, the two most popular approaches to storing and managing data are Data Warehouse and Data Lake. The choice between them usually depends on business objectives and needs. While Data Lakes are ideal for preserving large volumes of diverse data, warehouses are more favorable for business intelligence and reporting. Sometimes, organizations try to have the best of both worlds and mix Data Lake & Data Warehouse architectures. This, however, can be a time and cost-consuming process.

Against this backdrop, a new hybrid approach—Data Lakehouse—has emerged. It combines features of a Data Lake and a Data Warehouse, allowing companies to store and analyze data in the same repository and eliminating the Data Warehouse vs. Data Lake dilemma. Data Lakehouse mixes the scalability and flexibility of a Data Lake with the ability to extract insights from data easily. Ever so compelling, this approach still has certain limitations. It should not be treated as a “one-size-fits-all” solution.

Read on for an explanation of each of these three styles, including their pros and cons.

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Converting Numbers into Roman Numerals with C#

Published 2024-06-11 by Kevin Feasel

Adron Hall changes numbering schemes:

Today, I’ll dive into a fascinating challenge: converting modern numbers into their ancient Roman numeral counterparts. The task is straightforward but intricate, involving a programming challenge that takes any positive integer from 1 to 3999 and converts it into the corresponding Roman numeral.

To convert regular decimal numbers into Roman numerals, one must follow a set of rules based on the values and combinations of specific Roman numeral characters. Here’s a brief summary of the conversion process:

Click through for the translation rules and how to do this in C#. Now I’m thinking about how to do this in F# and thinking tail-call recursion. I might give that a try on my own and blog about it if I come up with something neat.

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