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Category: Learning

Measure Your DBA Skills

Lee Markum has just wrapped up an interesting series:

Over the last 9 weeks I took you on a journey of skills and career topics related to being a SQL Server DBA. We looked at the Production DBA. We saw skills and career topics from the beginning to mid-career to Senior DBA. Then we looked at the Development DBA and their skills and career development needs. Finally there was a wrap up post.

To make it easier for everyone to get to these posts, I decided to bring them all together on a single page.

Click through to get a feeling for where you’re at on the DBA and database developer sides of the house.

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Data Professional Salary Survey

Brent Ozar has another year of the Data Professional Salary Survey:

Take the Data Professional Salary Survey now.

The anonymous survey closes Friday, Jan 1, 2021. The results are completely open source, and shared with the community for your analysis. (You can analyze ’em now mid-flight, but I’d wait until the final results come in. I’ll combine them into a single spreadsheet with the past results.)

I’ve had fun analyzing it over the years. If you wouldn’t mind, please fill it out and add some more data points.

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The DevOps Learning Curve

Grant Fritchey gives us the low-down on learning about DevOps:

If you’re attempting to implement automation in and around your deployments, you’re going to find there is quite a steep learning curve for DevOps and DevOps-style implementations. Since adopting a DevOps-style release cycle does, at least in theory, speed your ability to deliver better code safely, why would it be hard?

Click through for an idea, including tools to use and some first steps.

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The Spark Starter Guide

Landon Robinson has some good news for us:

If you visit hadoopsters.com/spark or thesparkguide.com, you’ll see something new and exciting from us. It’s official: we’ve written and are publishing a comprehensive guide to Apache Spark.

This guide will be completely online and completely free. A book’s worth of content, containing exercises in Python and Scala to teach you Spark, at your fingertips. Again, free.

Landon has posted chapter 1, section 1 already:

This section introduces the concept of data pipelines – how data is processed from one form into another. It’s also the generic term used to describe how data moves from one location or form, and is consumed, altered, transformed, and delivered to another location or form.

You’ll be introduced to Spark functions like joinfilter, and aggregate to process data in a variety of forms. You’ll learn it all through interactive Spark exercises in Scala and Python.

This is very early in the process but I’m excited.

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Azure SQL Championship

Mala Mahadevan announces a contest:

Learning can be drudgery, it can also be fun. One of the fun ways to learn Azure is to take part in Azure SQL Championship – a joint attempt by Microsoft and PASS to promote Azure learning. From October 12-30, there will be daily quizzes/simple challenges to solve. If you do it right you have a chance to win some fabulous prizes as below:

Read on to learn more, including the prizes on offer.

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Learn Powershell with PSKoans

Mikey Bronowski shows off PSKoans:

Recently I have got a question about resources to learn PowerShell. There is plenty out there in the wild, but I came across an interesting module I would like to write today – PSKoans.

I’m a big fan of the koan strategy of learning. It ramps you up slowly and gives you plenty of code to help understand syntax and flow. The F# koans are fantastic, as are Python’s.

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A Critique of “Advanced” as a Description of Learning Content

Don Jones lays out the argument for why the term “advanced” doesn’t mean much for learning:

Let me share a little secret of the training industry with you: an “advanced” topic is any topic that you don’t already know.

Don’t argument is that the proper axis is around commonality of usage: most commonly performed to least commonly performed. It’s an interesting argument for sure.

I’m of two minds with the idea, however. I appreciate Don’s example and like the concept of commonality for differentiation. But there are things which are legitimately advanced topics, in that they would be difficult to understand even if they were common. In Don’s query tuning example, an example of something legitimately difficult to understand is the set of rules the query optimizer chose to test for a particular query. Yes, it is very uncommon to need to know this, but it is also difficult to understand if you do need to know, and explaining how and why the query optimizer chose the path and rules that it did requires a fairly deep base of expertise.

In short, I think there’s an endogeneity problem: things can be perceived as difficult because they are unommon (which is my reading of Don’s point), but also things can be uncommon because they are difficult to understand given some baseline of knowledge.

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