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Day: January 11, 2024

The Triangular Distribution in TidyDensity

Published 2024-01-11 by Kevin Feasel

Steven Sanderson unleashes the power of the triangle:

Welcome back, fellow data enthusiasts! Today, we embark on an exciting journey into the world of statistical distributions with a special focus on the latest addition to the TidyDensity package – the triangular distribution. Tightly packed and versatile, this distribution brings a unique flavor to your data simulations and analyses. In this blog post, we’ll delve into the functions provided, understand their arguments, and explore the wonders of the triangular distribution.

Read on to learn what the triangular distribution is and how you can use work with it in TidyDensity.

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Shortcuts in Microsoft Fabric

Published 2024-01-11 by Kevin Feasel

Koen Verbeeck takes a shortcut:

A while ago I had a little blog post series about cool stuff in Snowflake. I’m doing a similar series now, but this time for Microsoft Fabric. I’m not going to cover the basics of Fabric, hundreds of bloggers have already done that. I’m going to cover little bits & pieces that I find interesting, that are similar to Snowflake features or something that is an improvement over the “regular” SQL Server or related products.

In this blog post I’m going to talk about shortcuts. 

Read on to learn more about this feature.

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Implicit Join Elimination in JooQ

Published 2024-01-11 by Kevin Feasel

Lukas Eder talks about implicit join elimination:

One of jOOQ’s key features so far has always been to render pretty much exactly the SQL that users expect, without any surprises – unless some emulation is required to make a query work, of course. This means that while join elimination is a powerful feature of many RDBMS, it isn’t part of jOOQ’s feature set, so far.

As Lukas mentions, many relational database products already do this–SQL Server is an example of one product that does. But not all of them do, so it’s nice to have that option available in the data access layer.

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Comparing Fabric F2 to F64

Published 2024-01-11 by Kevin Feasel

Reitse Eskens enters austerity mode:

If you’ve been having fun with Microsoft Fabric, chances are you’ve been playing around with the F64 capacity trial. This one is given to you by Microsoft for free but, since the GA data, the timer attached to it is counting down the days until you need to buy your own.

Read on to see what happens when you lose out on that sweet F64 goodness. I actually do appreciate the way that Fabric works: it’s not a linear scale of “F2 means you get 1/32 the processing power of F64.” Rather, it’s closer to time slices on a mainframe: F64 gets you a bigger slice. So if you’re a small shop without an enormous amount of data, F2 really does work pretty well.

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A Primer on Direct Lake

Published 2024-01-11 by Kevin Feasel

Ginger Grant talks about a Fabric feature not in Power BI or Synapse:

With the general availability release of Fabric in November 2023, I am dedicating several posts to the features that are only in Fabric and not anywhere else. The first feature is Direct Lake. Direct Lake was created to address problems with Power BI Direct Query. Anyone who has used Direct Query knows what I am talking about. If you have implemented Direct Query, I am guessing you have run into one or all of these problems, including managing the constant hits to the source database which increase with the more users you have, user complaints about slow visuals, or the need to put apply buttons on all of your visuals to help with speed. Direct Query is a great idea. Who wants to import a bunch of data into Power BI? Directly connecting to the database sounds like a better idea, until you learn that that the data goes from Power BI to the database then back for each user one at a time, which means that Power BI must send more queries the more people are accessing reports. Users want to be able to access data quickly, have it scale well, and have access to the latest data.

Click through to learn more about Direct Lake.

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Logical Replication in Postgres

Published 2024-01-11 by Kevin Feasel

Muhammad Ali takes us through replication in Postgres:

PostgreSQL provides two main types of replication: Physical Streaming Replication and Logical Replication. In this blog post, we explore the details of Logical Replication in PostgreSQL. We will compare it with Physical Streaming Replication and discuss various aspects such as how it works, use case, when it’s useful, its limitations, and key points to keep in mind.

Logical replication is the Postgres equivalent to SQL Server replication. Read on to see how it works.

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