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Category: Misc Languages

Case-Insensitive Searches in Snowflake

Koen Verbeeck shows how you can perform case-insensitive searches in Snowflake DB:

I’m doing a little series on some of the nice features/capabilities in Snowflake (the cloud data warehouse). In each part, I’ll highlight something that I think it’s interesting enough to share. It might be some SQL function that I’d really like to be in SQL Server, it might be something else.

Today I have a small blog post about a neat little function I discovered last week – with thanks to my German colleague, who wants to remain anonymous. The function is called ILIKE and it is syntactic sugar for the combination of UPPER and LIKE.

I’m personally not a fan of case-sensitive collations for data; it’s hard for me to understand the meaningful differences between “dog,” “Dog,” and “DOG.”

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Using the ML.NET Model Builder

I have a post looking at the ML.NET Model Builder:

You have four options from which to choose: two-class classification, multi-class classification, regression, or Choose Your Own Adventure. Today, we’re going to create a two-class classification model. Incidentally, they’re not kidding about things changing in preview—last time I looked at this, they didn’t have multi-class classifiers available.

Once you select Sentiment Analysis (that is, two-class classification of text), you can figure out how to feed data to this trainer.

I think this is fine for developers who are looking to add a machine learning component as a small part of a bigger product. I don’t think it will beat a trained human using R or Python, but it’s an interesting avenue.

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Creating Models with ML.NET

I have a series on ML.NET; in this post, I look at building a model:

Okay, now that I have classes, I need to put in that lambda. I guess the lambda could change to qb => qb.Quarterback == "Josh Allen" ? "Josh Allen" : "Nate Barkerson" and that’d work except for one itsy-bitsy thing: if I do it the easy way, I can’t actually save and reload my model. Which makes it worthless for pretty much any real-world scenario.

So no easy lambda-based solution for us. Instead, we need a delegate. 

The experience so far has been a bit frustrating compared to doing similar work in R, but they’re actively working on the library, so I’m hopeful that there will be improvements. In the meantime, I’ve landed on the idea of doing all data cleanup work outside of ML.NET and just use the simplest transformations.

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Using Java in SQL Server 2019

Niels Berglund has an update on writing Java code in SQL Server 2019:

In CTP 2.5 and onwards when you write Java code for SQL Server you implement your code using the Microsoft Extensibility SDK for Java, (SDK). The SDK acts sort of like an interface as it exposes abstract classes that your code need to extend/target, (more about that later).

The SDK comes in the form of a .jar file, and you download the SDK from here.

Niels dives deep into the topic, so set aside a bit of time to read through this one.

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Bash Script Introductions

Kellyn Pot’vin-Gorman continues a series on Bash scripting:

For Part II, we’ll start with the BASH script “introduction”.

The introduction in a BASH script should begin the same in all scripts.
1. Set the shell to be used for the script
2. Set the response to failure on any steps, (exit or ignore)
3. Add in a step for testing, but comment out or remove when in production

For our scripts, we’ll keep to the BASH format that is used by the template scripts, ensuring a repeatable and easy to identify introduction.

Click through to see what that entails.

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Problems Distributed Systems Experience

RJ Zaworski gives us examples of the types of problems you can run into with distributed systems:

Time limits: ending the neverending
Here’s one to ponder: how long can a long-running action go on before the customer (even a very patient, very digital customer) loses all interest in the outcome?
Pull up a chair. With no upper bound, we could be here a while.

Read on for more in that vein with JavaScript-y solutions.

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Azure Cloud Shell

Mark Broadbent gives us an introduction to Azure Cloud Shell:

There are two ways to access Azure Cloud Shell, the first being directly through the Azure Portal itself. Once authenticated, look to the top right of the Portal and you should see a grouping of icons and in particular, one that looks very much like a DOS prompt (have no fear, DOS is nowhere to be seen).

The second method to access Azure Cloud Shell is by jumping directly to it via shell.azure.com which will require you to authenticate to your subscription before launching. There is an ever so slight difference between each method. Accessing the Shell via the Azure Portal will not require you to specify your Azure directory context (assuming you have several) since your Portal will have already defaulted to one, whereas with the direct URL method that obviously doesn’t happen.

Read the whole thing.

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Monads and Monoids and Functors

Anmol Sarna explains the concept of a monad:

In functional programming, a monad is a design pattern that allows structuring programs generically while automating away boilerplate code needed by the program logic.

To simplify the above definition a bit more, We can think of monads as wrappers. You just take an object and wrap it with a monad.

Let’s just be clear on one thing: A Monad is not a class or a trait; Neither is it only dedicated to the Scala language. It is a concept related to functional programming.

This also includes a few examples in Scala.

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Reviewing the Stack Overflow Developer Survey

Michael Toth looks at the recently-released 2019 Stack Overflow Developer Survey:

Since 2011, Stack Overflow has been surveying their users each year to answer questions about the technologies they use, their work experience, their compensation, and their satisfaction at work. Given Stack Overflow’s place in the broader programming world, they are able to draw quite the audience for their annual surveys.

This year, nearly 90,000 developers participated in the survey! There’s a lot in this survey, and I recommend reviewing it yourself, but I wanted to surface some of the key findings that I thought were particularly relevant to data professionals here.

Stack Overflow says they will be releasing the underlying data for this survey in the coming weeks, so I hope to return to this for a deeper analysis once that’s made available. For now, let’s get into the results!

Michael’s lede involves R versus Python in terms of salaries, but for me, the top line is that functional programmers make more money. Clojure, F#, Scala, Elixir, and Erlang make the top 10 on the global list, including positions 1, 2, 4, and 5. Within the US, Scala, Clojure, Erlang, Kotlin, F#, and Elixir make the top 10, including positions 1, 2, and 4. H/T R-Bloggers

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