Redgate at SQLBits

Posted on March 3, 2022 by way0utwest

SQLBits LogoRedgate and the Advocates are attending SQL Bits next week in London. I’m excited to see Grant and Kathi in person, as it’s been far too long since I have been in the same room with them.

You can register today and attend any of these sessions in person.

If you feel comfortable in crowds and miss getting together in person, come to SQL Bits next week and say hi.

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Traveling to SQL Bits

Posted on March 2, 2022 by way0utwest

I’m heading to SQL Bits this weekend, and will be in London most of next week for the conference. I’m excited. SQL Bits is my favorite conference, as I’ve noted before. I’ve missed the event too often, usually because of schedule conflicts, and I’m thrilled to be heading back to both London and the event.

I’ll be delivering a pre-con on Wednesday, with Grant and Kathi, and have a couple sessions. Thursday. The schedule is packed with some amazing speakers. Some will be virtual, but lots of us will be live, in-person.

If you are near London, and feel safe, consider registering and coming out. Lots of precautions are in place, and I think they have done a good job of preparing for a large conference.

With the pandemic, travel has been restricted and plenty of people have been cautious. Across two years, I’ve felt a range of emotions, but I feel confident in my safety, and that of others, at this time.

This should be a fantastic time, and I look forward to seeing friends and meeting new people next week.

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Data Analysis Techniques

Posted on March 2, 2022 by way0utwest

This is part of a series on my preparation for the DP-900 exam. This is the Microsoft Azure Data Fundamentals, part of a number of certification paths. You can read various posts I’ve created as part of this learning experience.

I didn’t think much of this bullet on the DP-900 skills document: describe analytics techniques.

Learn these concepts. They are definitely part of the knowledge needed. This is a part of some MS Learn courses, like this one.

Descriptive Analytics

Descriptive analytics have to do with describing what the data shows. In a report or visualization, we are reporting data to the user. An example of this is showing the top 10 products according to sales.

These are explanatory analytics and think about that in the test. Are we explaining something.

Diagnostic Analytics

A diagnosis is an explanation of why something happened. Diagnostic analytics help explain the reason that something occurred. We might look at the top 10 sales against advertising for products and determine that more advertising shows higher sales.

In the link above from MS Learn, what we typically do here is identify some anomaly or thing we want to explain, get data related to this, and then use some statistics to find a relationship.

Predictive Analytics

As you might guess, this type of analytics relate to the future. We are looking to predict what will happen. An example might be forecasting future demand for services.

When you see a request to predict, forecast or extrapolate, think predictive analytics.

Prescriptive Analytics

This type of analysis was a little harder for me to understand or grasp. I get the general idea, but in prep I missed this one a few times. Prescriptive analytics are developing a prescription for how to change something.

An example might be if I want to find where to cut costs to impact profit, I am looking for a set of things to change. I need a prescription on what will help me achieve my goal.

In relation to the exam, this has to do with aiming for a goal (10% more or 12 less) and how to reach that goal. If you see specific numbers, usually that relates to a goal and prescriptive analytics.

Cognitive Analytics

This type of analysis has to do with thinking. In data analysis and computing, usually I think ML, AI, or some other type of data mining. This can be deriving conclusions or inferences from data, but the blend between this and the descriptive/diagnostic stuff becomes blurry.

If you need to do speech to text or video transcription or image recognition, think cognitive.

Summary

This is how I viewed this techniques on the exam when answering questions:

  • Descriptive
    – What

  • Diagnostic
    – Why

  • Predictive
    – What will happen

  • Prescriptive
    – What do I do

  • Cognitive – Learning about data in new ways
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The Test Mistake

Posted on March 2, 2022 by way0utwest

Last year a company was testing their software in a dev/test environment and accidentally sent out an “Integration email” to their customers.  They blamed this on an intern, though a little jokingly, and plenty of people shared their own stories about making mistakes.

I did this once, and only once. I was doing some software testing on a copy of the production database and accidentally sent out thousands of emails to customers. I hadn’t scrubbed the email addresses in the copy, but since I also hadn’t scrubbed the data, customers got an “extra” email for their account and few complained. My boss, however, complained quite a bit and with good reason.

These days using production data in dev/test environments isn’t really an acceptable practice. Plenty of organizations do it, and hopefully, they are taking precautions. Kendra Little wrote a little about what happened and what should be done, including scrubbing out sensitive data. Not enough people do this, though I am seeing more and more companies looking for solutions that prevent data release.

This isn’t an intern’s issue. I appreciate someone making a bit of a joke here, and hopefully, there were engineers that taught the intern something. The bigger issue is that we make mistakes as humans and our protocols and processes should expect that. This is one reason why adopting automation in our software process is important. Whether that’s deploying code to the right production server or refreshing the non-production databases, we want to be sure that we limit the number of human mistakes.

There are always places where we will use humans to perform a process. Whether that’s typing code or clicking something. It’s entirely possible someone clicks the wrong button or types the wrong item. As much as possible, we can use automation to provide a safety net to prevent these issues from reaching customers. We can’t prevent every mistake, but we can continue to improve our process but adjusting it over time. At the very least, we ought to prevent the same mistake from happening over and over.

Steve Jones

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