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Day: September 23, 2021

De Moivre’s Equation and Sample Size-Based Variance

Holger von Jouanne-Diedrich demonstrates de Moivre’s equation:

Over one billion dollars have been spent in the US to split up big schools into smaller ones because small schools regularly show up in rankings as top performers.

In this post, I will show you why that money was wasted because of a widespread (but not so well known) statistical artifact, so read on!

Do read on to learn more about this paradox.

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Visualization in Spark with Drsti

Jean-Georges Perrin shows off a Spark library:

I was looking for an effortless data visualization that would interface easily with Apache Spark. I found a few interesting tools, but nothing that would not require some complex interfacing, setup, or infrastructure. In a good geek way, I then decided to write the tool. This lack of simple tools is how Drsti (pronounced drishti) was born.

Aren’t you tired of looking at dataframes that looked like they came straight from a 1980 VT100? Sure, if you use notebooks, either standalone or hosted (IBM Watson Studio, Databricks…), you are not (or less) confronted with the issue. However, if you are building pipelines outside of the Data Science toys, oops, tools, you may need to visualize data in a graph.

Read on to see how it works and some of what you can do with Drsti.

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An Intro to Dapr

Steve Jones tries out Dapr:

I’ve heard about Dapr a few times from developer friends, but hadn’t really understood it that well. I had a webinar coming up, so I decided to spend a bit of time working with it to understand how it might function with an application.

I went to, and saw the basic outline of Dapr is in this video from their site. I also found this getting started video from Donovan Brown.

Note that Dapr is totally different from Dapper.

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ETL via Powershell

Greg Moore builds a simple ETL process using Powershell:

Recently a customer asked me to work on a pretty typical project to build a process to import several CSV files into new tables in SQL Server. Setting up a PowerShell script to import the tables is a fairly simple process. However, it can be tedious, especially if the files have different formats. In this article, I will show you how building an ETL with PowerShell can save some time.

It’s a simple process, but that’s a good reminder that simple processes can be good processes.

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Sean Gallardy lays out what HADR_SYNC_COMMIT really tells you:

Initially I thought to myself, “this is the most misunderstood wait type that exists in the HA space for SQL Server”, then I realized maybe this isn’t the case… So, I pondered over this question, “is it truly misunderstood?” and came to the (possibly incorrect) realization that it is quite accurate in the general SQL Server’s users’ space of understanding. I also concluded that, really, it’s the way the wait is used in SQL Server coupled with how waits work in SQL Server, which leads to how it is viewed. Let me explain….

You’ll definitely want to read Sean’s explanation.

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Automating Notebook Execution with Powershell

Julie Koesmarno shows off an automation process for notebooks:

When I first think about automation, I generally think in the following way: in order to automate a script, we want to ensure that the script itself can be run via a command line interface (CLI) and with almost no user interaction (except for input and output parameters). Now, how do we apply this to Jupyter Notebooks so that we can automate SQL notebooks or PowerShell Notebooks?

The good news is that these SQL notebooks and PowerShell notebooks that we’ve created using Azure Data Studio, can be run on PowerShell CLI. If these notebooks can be run on PowerShell CLI, that means any automation systems or serverless architecture (Azure Automation combined with Azure Logic Apps as an example) should be able to run these notebooks also.

In this blog post, I’ll cover examples on using Invoke-SqlNotebook, using Invoke-ExecuteNotebook and putting it together with Azure Automation.

Click through to see the whole thing.

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Thinking Twice about Single-Column Indexes

Erik Darling wants you to perform a sanity check:

There are times when a single key column index can be useful, like for a unique constraint.

But for the most part, outside of the occasional super-critical query that needs to be tuned, single key column indexes either get used in super-confusing ways, or don’t get used at all and just sit around hurting your buffer pool and transaction log, and increasing the likelihood of lock escalation.

Read on for Erik’s full point. Sometimes that single-column non-clustered index really does do the trick—as in a unique key constraint, or a single column used in a really commonly-used EXISTS clause—but it’s worth thinking about whether that one column is really all there is.

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Up or Out: Promotion Paths

Bob Pusateri describes a common promotion path:

But one piece of career growth that I’ve never gotten from any employer is a promotion.

Allow me to define “promotion”. In my mind, a promotion is where an employee earns a change in job title with commensurate increase in responsibility. I won’t even say it has to include an increase in pay, though I imagine it often would. Similarly, a promotion need not involve moving into a management position. Simply put, to me a promotion is doing a great job as a junior widget maker and then one day being told your hard work and contributions have been noticed and you are no longer junior.

This is pretty common. As an example of the other side of the coin, I’ve been promoted (using Bob’s definition) at two separate companies. But in a lot of cases, hierarchies in engineering teams have nearly disappeared, such that there’s no longer a Software Developer 1 who can be promoted into a Software Developer 2, and then a 3 and a 4, and then an Enterprise Architect 1, etc. Instead, out is the new up.

The funny part of the pattern is that I know people who have left a company in order to move up. Then, after some time with the new company, they return to the old company in a higher role.

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