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Curated SQL Posts

SQL Server on Linux 2022 Available in Preview

Published 2023-08-14 by Kevin Feasel

Amit Khandelwal has an update on SQL Server on Linux:

We are glad to announce that SQL Server 2022 is now available in preview mode for both Red Hat Enterprise Linux (RHEL) 9 and Ubuntu 22.04. For this preview, only Evaluation edition is available, which is limited to 180 days starting Thursday, July 27th, 2023. 

In your Dev/Test environments, you may now take advantage of the most recent SQL Server 2022 improvements on both RHEL 9 and Ubuntu 22.04. Currently, production workloads on RHEL 9 and Ubuntu 22.04 are not supported by the SQL Server 2022 preview packages. You can run the production workloads for SQL Server 2022 on RHEL 8 and Ubuntu 22.04 and they are fully supported. 

I’m going to wait until it’s actually available for real, not just in preview.

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Diving into the Microsoft Fabric Copy Activity

Published 2023-08-11 by Kevin Feasel

Reza Rad does more than copies:

Copy Activity is one of the most commonly used activities in Microsoft Fabric’s Data Factory Pipeline. The Copy Activity copies the data from a source to a destination. However, there is more to that rather than just a simple copy. In this article, you will learn what Copy Activity is, its rationale, how it works, and its configuration options.

Reza has a video, as well as a demo-heavy full-length article on the topic.

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Executing SQL Queries in Files against Postgres

Published 2023-08-11 by Kevin Feasel

Salman Ahmed automates query execution:

In PostgreSQL, there are several ways to execute queries, and one of them is by executing queries from SQL files. This approach allows users to manage and store their SQL queries separately and make debugging and development simpler. Using SQL files also helps in replication of database schemas. This blog discusses how to execute queries from SQL files in PostgreSQL.

Read on to see how you can use the psql command line tool to do just that.

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Creating a Simple Date Dimension in Databricks

Published 2023-08-10 by Kevin Feasel

Chen Hirsh builds a table:

A date dimension is extremely useful and is required by most BI applications. This kind of dimension has a key of time level (day, month, etc.), and attributes that describe it such as year, month, etc. In your BI model, you join this dimension to facts on their date fields, to aggregate from day level to week, month, and year.

In this post, I will demonstrate how to create a date dimension on Azure Databricks using Python. A link to the complete Databricks notebook is at the end of the post.

Check out the code, as well as explanation, in that post.

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Managing Plot Parameters in R

Published 2023-08-10 by Kevin Feasel

Steven Sanderson switches up a visual:

When it comes to data visualization in R, the par() function is an indispensable tool that often goes overlooked. This function allows you to control various graphical parameters, unleashing a world of customization possibilities for your plots. In this blog post, we’ll demystify the par() function, break down its syntax, and provide you with hands-on examples to help you create stunning visualizations.

Click through to check it out. My loyalties definitely lie with ggplot2 for static visual development in R but it’s definitely not the only way to get images to look the way you want them.

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Combining Cosmos DB and Azure Search

Published 2023-08-10 by Kevin Feasel

Hasan Savran does some looking:

In my previous post, I discussed the process of establishing a Free-text search for Azure Cosmos DB. Towards the end, I demonstrated how to carry out a free-text search using the Azure Portal. Now, I will guide you on how to perform this search using code. To perform this search by code, I created a basic console application and added Azure.Search.Documents and Microsoft.Azure.Cosmos.

Click through for that demonstration.

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From Join to Lookup in KQL on Power BI

Published 2023-08-10 by Kevin Feasel

Dany Hoter gives us a workaround:

Many users who try  ADX in direct query mode encounter errors right away.

The errors  complain about lack of memory.

 If the tables are small enough, it may work but still performance will not be as advertised on TV.

The reason in most cases is the behavior of joins in ADX as they are created by PBI.

In this article I’ll show different approaches to joining tables as used by PBI for related tables or as can be expressed in KQL in general.

I created a special table in the help cluster with 31 million rows that is big enough to demonstrate the performance differences between the variations.

Read the whole thing. This one’s a little surprising to me.

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