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Author: Kevin Feasel

A Git Cheat Sheet

Published 2019-12-03 by Kevin Feasel

Kendra Little has a cheat sheet for working with Git:

I created a cheat sheet for the Git Command Line Interface to go along with my Git tutorial for SQL Change Automation video. I find the Git CLI to be very friendly and easier to learn than a GUI interface.

Given the number of “How do I extricate myself from this Git mess?” messages in my company chat, I’m not sure I’d call the Git CLI friendly. Nonetheless, Kendra does a great job of putting together most of the common commands in an easy guide.

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United States Maps in R

Published 2019-12-02 by Kevin Feasel

Laura Ellis shows how to use the usmap package in R:

Today, I’d like to share the package ‘usmap’ which enables incredibly easy and fast creation of US maps in R.

In honor of US Thanksgiving tomorrow, I’m going to make this blog Thanksgiving themed! In this tutorial, we will use the gTrendsR package to pull US Google search results on the keyword “thanksgiving” and plot the popularity by state.

Click through for that demo, as well as links to more demos on map usage.

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Common Mistakes When Moving to the Cloud

Published 2019-12-02 by Kevin Feasel

Dave Wentzel takes us through common issues companies experience when adopting a cloud provider:

Don’t make these mistakes:

– Don’t try to use pricing calculators and expect their answers to be close to what your actual spend will be. Cloud expenses are buried everywhere. Instead, have a rough budget to move ONE app to the cloud. Migrate it. Wait a month and examine the bill. What line items were you NOT expecting to see? Is data egress higher than you thought? That’s common. Now, how can you creatively fix that?

– PaaS is never cheaper, at least initially. I call this The PaaS Tax. It will cost you more to use PaaS than to run the same workload in IaaS. Initially. Remember, the paradigm is different from “datacenter” to “cloud”. PaaS becomes cheaper when you leverage PaaS scaling. Since you can’t really scale something like SQL Server in your data center, most people forget this. But in the cloud you can scale down your SQL Server when it is lightly used. That’s how you save money.

Click through for the full story.

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Comparing Dates in a WHERE Clause

Published 2019-12-02 by Kevin Feasel

Erik Darling has been diving into issues with date comparison lately, including comparing date columns in the WHERE clause:

A common dilemma is when you have two date columns, and you need to judge the gap between them for something.

For instance, say you have a table of orders and you want to figure out how long on average it takes an ordered item to ship, or a shipped item to be delivered.

You’re not left with many good ways to write the query to take advantage of indexes.

Read on to see what Erik means, and one way you can speed it up a bit.

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Structuring Databricks Notebooks

Published 2019-12-02 by Kevin Feasel

Paul Andrew has put together a basic structure for Databricks notebooks using titles, markdown, and widgets:

For me, one of the hardest parts of developing anything is when you need to pick up and rework code that has been created by someone else. That said, my preferred Notebook structure shown below is not about technical performance or anything complicated. This is simply for ease of sharing and understanding, as well as some initial documentation for work done.

In my example I created a Scala Notebook, but this could of course apply to any flavour.

This makes good use of markdown capabilities without being too heavy. I like it. The same general principles apply if you’re putting together Jupyter notebooks outside of Databricks.

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Editing Rows in SSMS–Behind the Scenes

Published 2019-12-02 by Kevin Feasel

Randolph West explains what happens when you use the editor built into SQL Server Management Studio to modify rows directly:

A customer recently brought up an interesting thesis, that if you edit a table’s values using SQL Server Management Studio (SSMS) using the edit feature, that the table is dropped and recreated in the background when you commit the changes.

This is false, but there had to be a good reason why they were under this misapprehension.

Read on to understand what does happen and where the customer might have gotten this idea.

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The Problems with RDPing into SQL Server Hosts

Published 2019-12-02 by Kevin Feasel

Emanuele Meazzo enumerates the issues with using RDP to connect directly to a server hosting SQL Server (or any other database system):

When there are SERIOUS performance problems, i.e. your instance is totally pinned as far as resources go, the only way to log into the instance to do something about it is using DAC; even if you are on the same machine, if you’re trying to log in, as usual, it won’t make any difference.
Enable Remote DAC and save yourself from future troubles, now.

There are several problems. Better is to run client tools from a client machine and something like SSH or Powershell remoting to do things against a server itself.

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Testing SQL Server Storage Performance

Published 2019-12-02 by Kevin Feasel

Brent Ozar walks us through CrystalDiskMark 7 to check whether storage speed is up to snuff:

The Peak Performance + Mix setting runs a pretty cool mix of tests that will push your storage hard. Note that I don’t try to get CDM to replicate exactly how SQL Server does IO: I’m just trying to get a quick 5-minute idea of whether my storage is hot or not.

Click through for the demo. Mind you, this is something you want to do before setting up SQL Server…

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Beginner’s Guide to Azure Data Factory

Published 2019-12-02 by Kevin Feasel

Cathrine Wilhelmsen has started a new series:

Azure Data Factory = Azure Data Factory v2

This means that today, when I talk about “Azure Data Factory”, I refer to “Azure Data Factory v2” and skip the “v2” part of the name. I mostly pretend that Azure Data Factory v1 doesn’t exist anymore 🙂

That’s something we all do.

This first post is a quick “What is this product?” intro, giving us a basis for later posts.

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Visualizing Kafka Data Using D3

Published 2019-11-27 by Kevin Feasel

Mihalis Tsoukalos extracts, explores, and visualizes data (with D3) from a Kafka topic:

Now that you have your data in JSON format, you will use D3.js in order to visualize it. As JavaScript code is embedded in HTML files, the final version of the D3.js code can be found in visualize-spatial.html, which contains the following code:

D3 is extremely powerful, though that power comes with a fairly steep learning curve.

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