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Author: Kevin Feasel

Exporting Dynamics 365 Data into Delta Lake via Synapse Link

Published 2023-09-27 by Kevin Feasel

Jose Mendes performs a data migration:

It’s fair to say there have been some considerable changes in the Azure landscape over recent years.

This blog will show you how to configure Synapse Link to export D365 data in the Delta Lake format – an open-source data and transaction storage file format used in Lakehouse implementations.

Before you start considering using this approach, you will need to ensure you meet the following prerequisites (Microsoft documentation).

Read on for those prerequisites as well as a step-by-step guide on how to do it.

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Upgrading SQL Server Cloud VMs

Published 2023-09-27 by Kevin Feasel

Brent Ozar recommends you check your cloud provider’s VM listings:

If you’ve been in Azure or Amazon for a few years, you’re probably on old, slow hardware.

In the last 3 weeks, I’ve had two clients who’d both been early cloud adopters. When they’d migrated to the cloud, they both used Azure Ev3 VMs – at the time, a good choice for SQL Server due to its relatively high amount of memory. When the Ev3 VM types were announced in 2017, they were hosted on Intel Broadwell and Haswell processors with 2.3-2.4GHz processing speed.

Also, even if you’re locked into a 1-year or 3-year deal, I know that at least Azure is usually willing to switch your VM class registration if you contact your support person. I’m not positive if AWS does the same but it wouldn’t shock me.

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Data Exploration in R with dplyr

Published 2023-09-26 by Kevin Feasel

Adrian Tam continues a series on R:

When you are working on a data science project, the data is often tabular structured. You can use the built-in data table to handle such data in R. You can also use the famous library dplyr instead to benefit from its rich toolset. In this post, you will learn how dplyr can help you explore and manipulate tabular data. In particular, you will learn:

  • How to handle a data frame
  • How to perform some common operations on a data frame

I like dplyr a lot for its “functional flow”—you pipe outputs of one function to be inputs of the next function, so the chain makes a lot of sense. If you want high performance, though, it’s often not the best choice—that’s usually data.table.

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ggplot2 in Python Notebooks

Published 2023-09-26 by Kevin Feasel

John Mount runs R in Python with rpy2:

For an article on A/B testing that I am preparing, I asked my partner Dr. Nina Zumel if she could do me a favor and write some code to produce the diagrams. She prepared an excellent parameterized diagram generator. However being the author of the book Practical Data Science with R, she built it in R using ggplot2. This would be great, except the A/B testing article is being developed in Python, as it targets programmers familiar with Python.

As the production of the diagrams is not part of the proposed article, I decided to use the rpy2 package to integrate the R diagrams directly into the new worksheet. Alternatively, I could translate her code into Python using one of: Seaborn objects, plotnine, ggpy, or others. The large number of options is evidence of how influential Leland Wilkinson’s grammar of graphics (gg) is.

Click through to see how you can execute R code within the context of Python, similar to how you can use the reticulate package to execute Python code in the context of R.

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Pairs Plots in Base R

Published 2023-09-26 by Kevin Feasel

Steven Sanderson shows how we can create a pairs plot using the pairs() function in R:

A pairs plot, also known as a scatterplot matrix, is a grid of scatterplots that displays pairwise relationships between multiple variables in a dataset. Each cell in the grid represents the relationship between two variables, and the diagonal cells display histograms or kernel density plots of individual variables. Pairs plots are incredibly versatile, helping us to identify patterns, correlations, and potential outliers in our data.

Click through for one example, how to interpret it, and how to customize the outputs.

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Creating an Image Classification Model in Oracle OCI Vision

Published 2023-09-26 by Kevin Feasel

Brendan Tierney separates the cats and the dogs:

In this post, I’ll build on the previous work on preparing data, to using this dataset as input to building a Custom AI Vision model. In the previous post, the dataset was labelled into images containing Cats and Dogs. The following steps takes you through creating the Customer AI Vision model and to test this model using some different images of Cats.

This post is part four of a series (first part, second part, third part) on custom image classification in Oracle.

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WITHIN GROUP in STRING_AGG()

Published 2023-09-26 by Kevin Feasel

Chad Callihan messes with groups:

When was the last time you wrote a SQL query and knew something was possible but just couldn’t remember how? I had one of those moments this week with STRING_AGG and ordering data, and although it was frustrating, I knew it would make a worthwhile blog post. Let’s look at some examples using STRING_AGG and WITHIN GROUP (aka the clause that slipped my mind).

There’s a perfectly good reason why WITHIN GROUP might slip your mind: STRING_AGG() is known as an ordered set function (versus a window function which uses an OVER() clause). It’s also the only ordered set function SQL Server supports, so you don’t get too many opportunities to use the key phrase.

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Setting a Spark Compute Pool Size in Microsoft Fabric

Published 2023-09-26 by Kevin Feasel

Reitse Eskens manages compute pools:

This next blog won’t be a long one and will probably serve most as a reminder for myself where to find the settings for the Spark compute pool.

When you create a workspace, you get the default starter pool and it has taken me way longer than I care to admit to find where to find the setting and, more importantly, how to change it.

Read on to learn more about how to create a Spark pool of the size you desire. The sizing method is essentially the same as with Azure Synapse Analytics.

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Generating Reproducible Reports with Jupyter and Quarto

Published 2023-09-25 by Kevin Feasel

Parisa Gregg and Myles Mitchell don’t need to copy and paste for their TPS reports:

Quarto is a free-to-use, open-source software based on Pandoc that enables users to convert plain text files into a range of formats, including PDF, HTML and powerpoint presentations. These documents can contain a mixture of narrative text, Python code, and figures that are dynamically generated by the embedded code.

This has many use-cases:

  • Your company may have a weekly board meeting to go over the latest sales figures. By having a Quarto presentation that pulls in the latest company sales data, you can regenerate the presentation slides each week at the click of a button.
  • As a researcher you may be preparing a report for publication. By having the code that generates data tables and figures embedded within the report, regenerating the draft as the experimental data floods in is a breeze!

Read on for a fun example of how you could automated a research-driven report.

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