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Day: October 9, 2023

Package Management in Python

Georgia Atkinson wraps things up with a bow:

Python is a general purpose, high level language which, thanks to its simplicity and versatility, has become very popular, especially within the data science community. The extensive Python community has developed and contributed thousands of libraries and packages over the years in a plethora of different disciplines to aid developers with their applications. Managing these packages can be a challenging task without the correct tools. That’s where Python package managers come in. In this blog post we will explore what a package manager is and why they are important. We will then cover some popular examples, including how to use them, how to install them and the pros and cons of each.

Whilst we will briefly touch on virtual environments in places, we will explore these in more depth in an upcoming post.

Read on for a primer on three options, including how they compare to one another for CI/CD purposes.

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Power BI Themes and Gallery

Seth Bauer has an announcement:

The Power BI Tips Theme Generator tool already allows you to easily interact with, and adjust, all the visual properties, wireframes, etc… How could we possibly make Power BI Theme building an effortless experience? We start with building it all for you, then letting you adjust it!
The all new Gallery feature represents a significant leap forward in simplifying the theming process for all. This feature is especially for the business users! But, it also opens up exciting opportunities for the Power BI community to contribute in the future as well.

Read on to see how this works.

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Vacuuming in PostgreSQL

Muhammad Ali keeps things tidy:

If you’re a PostgreSQL user, you’ve undoubtedly come across the term “vacuum“. This operation plays a pivotal role in maintaining the optimal performance of your database while preventing unnecessary data bloat. In this blog, we’ll understand how vacuum works on high level, its significance, types, server parameters that influence autovacuum operations, and general FAQ’s on vacuum.

Read on to learn more about what vacuuming does and why it is important. It also turns out that there are multiple types of vacuuming.

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SortByColumn Set to Invalid Column ID in SSAS Tabular

Olivier Van Steenlandt troubleshoots an error:

After making these changes, we pushed our changes into Azure DevOps and our deployment pipeline started to deploy the changes to the requested environment.

While the deployment process was executing, it stopped and failed promptly. We ran into an issue: “SortByColumn property set to an invalid column ID”

Read on to see the ultimate cause of and solution to the problem.

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In-Memory OLTP and Memory Allocation

Tanayankar Chakraborty explains an error:

We recently encountered a support case where a customer using In-memory tables in an Azure SQL DB, receives an error message while trying to insert data into the table that also has a clustered columnstore index. The customer then deleted the entire data from the In-memory Tables (With the clustered columnstore index), however it appeared that the Index Unused memory was still not released. Here’s the memory allocation the customer could see:


In addition to the error above- here is the error text:

Msg 41823, Level 16, State 109, Line 1

Could not perform the operation because the database has reached its quota for in-memory tables. This error may be transient. Please retry the operation. See ‘‘ for more information

In this case, the error ends up being a “didn’t read the manual” type of error.

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Workaround for Primary Keys in Fabric Data Warehouses

Gilbert Quevauvilliers needs a key:

When I started looking into using the data warehouses feature in Fabric, I did see that there were limitations on Primary Key columns.

Below is my blog post on how I still use keys in my data warehouse, instead of using GUID’s which to me are long and hard to use.

In my example I am going to create a simple data warehouse which is going to consist of two-dimension tables (Date and Country) and a fact table with the Sales amounts.

This seems sub-optimal, though at least Gilbert shows us a workaround.

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