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Day: April 6, 2022

Printing ggplot2 Plots as Receipts

Bob Rudis has fun with a Point of Sale printer:

At the end of March, I caught a fleeting tweet that showcased an Epson thermal receipt printer generating a new “ticket” whenever a new GitHub issue was filed on a repository. @aschmelyun documents it well in this blog post. It’s a pretty cool hack, self-contained on a Pi Zero.

Andrew’s project birthed an idea: could I write an R package that will let me plot {ggplot2}/{grid} objects to it? The form factor of the receipt printer is tiny (~280 “pixels” wide), but the near infinite length of the paper means one can play with some data visualizations that cannot be done in other formats (and it would be cool to be able to play with other content to print to it in and outside of R).

Read on for a fun story which gets an entry in my most coveted category. H/T R-Bloggers

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Fun with Powershell: A Roundup

Robert Cain has a bunch of posts:

Since September 2020 I’ve been posting extensively about PowerShell in a series I’ve titled Fun With PowerShell. In my next post I will begin a series on a new topic, but before I do I wanted to leave with a Fun With PowerShell Roundup” post.

Below you will find a listing of all posts in my Fun With PowerShell series. Note there are other posts I’ve done in this time frame, but since they did not focus on PowerShell I’ve omitted them from this listing.

Click through for a lengthy and good series.

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Diagnosing an Always Encrypted Decryption Issue

Reitse Eskens does some troubleshooting:

For those who are not familiar with Always Encrypted: it’s a built-in technique in SQL Server where data gets encrypted in a random or deterministic manner with a certificate and an algorithm. Long story short, without the certificate it’s gobbledygook. If you want to read more, check out this link.

As mentioned, the encryption was working perfectly, no-one could read the data in a usable way in the application. The point was that the application should be able to do that. And so we took up the challenge to see where things went wrong.

I’ve found that data is much more secure when nobody can ever see it again. That’s why I store all of my data in /dev/null. It’s also extremely fast.

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Performance Optimization for Azure Data Explorer

Ashok Anand Kumar has some performance tips:

Azure Data Explorer provides the capability to easily fetch telemetry data from a variety of data sources and run complex analytical queries. Azure Data Explorer supports both batch and streaming ingestion to support near real-time latency requirements. Batch ingestion will have latencies based on the batching policy and query frequency from applications. Streaming ingestion can be leveraged for low latency requirements. Data is cached and indexed for faster query performance and optionally exported out to Azure Data Lake in parquet format for batch processing and integration with other Big Data and Machine Learning (ML) services. 

Read on for several tips.

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