Category: Tools

Bob Pusateri talks tools:

I was in a meeting the other day where someone stated that “writing SQL isn’t real engineering.” I was taken aback by this, and initially didn’t even know how to respond other than to say that sometimes SQL is the best tool to solve a problem, and other times it isn’t. The blanket statement that one programming language is somehow better or more “real engineering” than another seemed incredibly narrow-minded. I’ve done some reflecting on this since, and wanted to share.

Everybody knows that Real Programmers Use FORTRAN.

This is an amazingly obtuse take—not Bob’s response, mind you, but the original statement that Bob is responding to. “Using Miter saws isn’t real carpentry” is the first response I had to it. Or maybe “Using culverts isn’t real civil engineering.” SQL is a domain-specific language for working with data, and it is the best domain-specific language for working with data. That’s why Feasel’s Law is a thing, after all.

Bob’s response is more measured and thoughtful than what I’ve put in here, so check that out.

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Erik Darling takes on the Royal We to announce updates:

We here at Darling Data strive to get things right the first time, but sometimes late nights and tired eyes conspire against us.

The nice thing about using these on a wide variety of SQL Servers in various states of disrepair is that bugs get spotted and sorted pretty quickly.

You can download all of the main SQL Server troubleshooting procedures I use in one convenient file.

Here’s a breakdown of changes you can find in the most recent releases!

Click through for quick changelogs for sp_QuickieStore, sp_PressureDetector, and sp_HumanEventsBlockViewer.

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The NDepend Blog has an interesting post on Visual Studio:

Notice that there are two distinct questions:

• Will Visual Studio Be Migrated to .NET Core? (but still remain Windows-centric).
• Will Visual Studio Be Multi-Platform?

Disclaimer: For nearly two decades, I’ve been the creator and lead developer of NDepend, a tool that functions both as a Visual Studio extension and a standalone app that can collaborate with VSCode and JetBrains Rider. NDepend also integrates into CI/CD pipelines to deliver interactive web reports on .NET code quality and security. We’ve collaborated with many Visual Studio engineers over the years, who have provided invaluable assistance, though we have no insider information. This post is based purely on observable facts and speculations derived from them.

Read on for those thoughts. My money’s on “No” for the latter, as that’s the purview of Visual Studio Code, and just thinking about the sheer amount of work necessary to make Visual Studio itself cross-platform, I can’t see it happening.

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Deborah Melkin shares a note about Power Apps:

When I write a SQL Query, I never have to worry if the way I’m writing a query will automatically create a limit on the number of records being returned if I’m not specifying TOP or OFFSET…FETCH. If my query wants to return 1 million records, it will return 1 million records.

PowerApps doesn’t work that way.

Click through to see how it does work, including some of the limitations around it.

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Mark Litwintschik tries out DuckDB:

DuckDB is an in-process database. Rather than relying on a server of its own, it’s used as a client. The client can work with data in memory, within DuckDB’s internal file format, database servers from other software developers and cloud storage services such as AWS S3.

This choice to not centralise DuckDB’s data within its own server, paired with being distributed as a single binary, makes installing and working with DuckDB much less complex than say, standing up a Hadoop Cluster.

The project isn’t aimed at very large datasets. Despite this, its ergonomics are enticing enough and it does so much to reduce engineering time that workarounds are worth considering. The rising popularity of analysis-ready, cloud-optimised Parquet files is removing the need for substantial hardware when dealing with datasets in the 100s of GBs or larger.

Read on to learn more about DuckDB, how it differs from SQLite, and a bit of nuttiness around how far you can push an in-memory database.

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Jenn Junod builds a flow chart:

From a structural perspective you can think of data as existing on a continuum or gradient: on the one side, there’s highly structured data, like customer profiles. Each customer has a known name, address, phone number, and we can expect those to fall within certain formatting constraints. For example, a phone number will always be a set of numbers and special characters. On the other side, there’s unstructured data: the contents of an email, for example, are unstructured.

The shape of the data should define what kind of software you use to store and manipulate it.

Read on for several questions along these lines and recommendations based on your answers.

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