2023 – T-SQL Tuesday

T-SQL Tuesday #165: What Do All The Database Job Titles Actually Mean?

Invitation and roundup from Josephine Bush

Earlier this year, when I was looking for a new job, I realized that there are a lot of conceptions about what different database jobs do. I was perplexed about what should be part of what job. These job titles could include other job title tasks, depending on what a company wants. The more I Googled what each of the job titles would include, the more it seemed there was no real standard for what each job title does. For example, if a company doesn’t have dedicated DBAs, that job function is lumped into another job title. You might be doing all of them at many companies because you are the db person, period.

My Conceptions or Misconceptions

Here’s what each job title means to me, whether accurate or not:

DBA – This is backups, restores, HA, DR, index maintenance, integrity checks, patching, upgrade, and maybe some performance tuning definitely at the server level, maybe at the query level
Database engineer – This is more coding and getting into the meat and bones of db design. This could also include building data pipelines. Also, it would include more query tuning.
Database reliability engineer – This is more DevOps-y than DBA, so it’s more about automation and creating CI/CD processes.
Database architect – This is designing database systems, such as architecting the infrastructure and making diagrams.
Data architect – This is intimately knowing the data inside and out and maybe designing data flows.
Data warehouse architect – This is a separate domain because DW OLAP differs greatly from DBA OLTP work. I don’t put this with DBRE or DE because it’s its own domain.
Data warehouse engineer – Is this a thing? Or is that lumped into a database engineer job title? But again, as I said with database architect vs. data warehouse architect, data warehouse engineer could be separate from database engineer.
Data scientist – This is modeling/predicting with datasets.

Kendra did an amazing post covering some of this last week. It was like she was reading my mind because I was preparing this post as she published hers! I hit her up about her post, and she suggested including data scientists in my post.

If you have other database job titles you want to include, please feel free. I thought up whatever I could based on what I saw or thought should be there. I’m excited to see what you all think!

T-SQL Tuesday #164: Code That Made You Feel A Way

Invitation and roundup from Erik Darling.

This month’s topic challenges you to think back to the last time you saw code that made you feel a thing. Hopefully a positive thing.

I’m not a thesaurus, so the list isn’t exhaustive, but think along the lines of: surprise, awe, inspiration, excitement.

Or maybe it was just code that sort of sunk its teeth into you and made you want to learn a whole lot more.

This won’t be my submission, but I think a great example is a piece of query tuning magic by Paul White. It was one of the first articles I ever read as a young SQL Developer.

It was one of many lightbulb moments (and, crap, I should have said lightbulb moment up there when I was telling you what kind of code I want you to think about) that I’ve had in my career.

The only rule I have is (outside of the normal T-SQL Tuesday rules) is that it can’t be your own code.

It should also probably be code that you won’t get sued for showing or talking about. Please be diligent in abiding by copyrights.

Other than that, it’s the normal pack of rabbits: submissions have to be posted next Tuesday, the 11th of July.

T-SQL Tuesday #163 Invitation – What is the best piece of Career Advice you ever received

Invitation and round up from Gethyn Ellis

There is no right or wrong answer here. It might be some technical advice like, “OK, so you got backups. You really need to be able to restore them, though have you tested that”

Or it might be something around the non-technical skills you need in this profession, like “Find a mentor”

We ask this question to our guests on our Putting the Human into Technology podcast, and the answers are always intriguing some of the short answers to the question include

Find a mentor
Be curious
Do what you love
Own the customer

These are just some of the answers we have had. There are many more

Feel the fear and do it anyway

This quote is now the title of a book. I googled it when writing this invite. I’ve not read the book, but the book was published in 2017. I got this advice circa September 2003. I think it’s a good title for a book. I might need to get a copy and have a read. Update the book Feel the Fear and do it Anyway by Suzan Jeffers was first published in 1987. So there is every chance that this came from that book. You can find the book here

So, Let me give some background. It was September 2003, and I worked for a local authority in Wales. I had just graduated from university. My Job title was Community Outreach Worker. Essentially, this was working on an online portal that the authority would use to promote local businesses and community organisations and their activities. It also allowed the council to bring some of their services online.

With this job I was working with technology and people, and that was always something I wanted to do, and in the round, I really enjoyed it. However, part of my role was phoning up people running community groups, asking them if they wanted to become involved in the session. It wasn’t quite cold calling, but it was close. I’ll be honest I didn’t look forward to doing this, I probably did anything else to get out of it, and I think this was obvious to most people on the team.

Let me Introduce Andy Wilson

I sat next to my colleague in the office, We’ve lost touch over the years, but his name was Andy Wilson. Andy doesn’t seem to be on social media or I’d tag him here. He was probably twenty years older than me with lots more experience. He was a great person, funny and good at his job. He could see I wasn’t having fun. So, he turned to me and said, “Look Geth, I can see you don’t like doing the phone calls. Why?” I explained that I didn’t like phoning people, and it made me feel a little nervous phoning up strangers. Andy turned to me and said, “Well, you have three calls to make. Why don’t you make them now, and they are done for the day, and you get on with the stuff you do enjoy doing… I got some advice from my old boss years ago when I worked in sales and had to make cold calls. He said to ‘Feel the Fear and do it anyway’ What’s the worst that can happen? They hang up on you? Which they won’t. They’ve signed up for a call back”

Did I make the calls?

So, I made the calls. Once I’d done one, I’d done two and ended up doing several more than the three on my list and feeling quite good about myself.

I’ve used this advice, mindset and how I felt afterwards that morning on many other occasions since. Whether changing jobs or moving into a DBA role for a large police force. Implementing a technical fix for a problem that you have been called out for at 3 am, and there is no one to escalate it to, you have to do something, and you’re in the land of do it now and ask for forgiveness in the morning and get the business working again or the business is stuck until 9 am when the rest of the business will be awake. I used the advice when making the Jump and started my own consulting business. It definitely helped when I was about to teach my first multi-day training class or deliver my first, second, third, and fiftieth conference sessions. When the nerves (or fear) kick in, I feel the fear and do it anyway! The feeling afterwards when you have done something out of your comfort zone is pretty amazing.

I’d say it’s had quite an impact on my career.

Summary – Over to you

I’d love to hear answers to the question, whether it’s around a technology choice, a career path, a company to work for, or whatever piece of advice you received that had the most significant impact on your career in the data profession, why I’d love to hear about it. You might help someone new to the data profession and give them an extra tool in their armoury to succeed on their path.

T-SQL Tuesday #162 – Data Science in the time of ChatGPT

Invitation from Tomaz Kastrun.

Instead of writing and asking Data science questions, let’s discuss the aspects of Data science with the presence of Chat GPT 4.0.

By now, it is known to everyone that Chat GPT is a language model (LLM – Large Language Model) that is based on the GPT (Generative Pre-trained Transformer) architecture. It uses deep learning algorithms to like neural nets with billions of weights and transformers, that generated the sequence of tokens, that make up a piece of text.Transformers introduce the concept of “paying attention” to generally build better sequence of text. It operates primarily with probabilities of words and their sequence and therefore it is also good for human-like responses to natural language queries, making it great for a conversation-like experience.

There are many of the caveats hidden in the processing of text, adjustments of weights, functions (different and tweaked versions of Relu), additional corpora and billions of text for model training and many additional texts.

I have prepared two groups of questions. And I will not go into debate, as the end of data science is near, nor will go into debate, that the AGI (artificial general intelligence) will completely replace the role of data scientists. What I want to hear from you is simply how did you embrace (if at all) the use of Chat GPT, and what were your first impressions. And mostly, how did it help you (if at all), what did you use it for, and have you encountered any traps?

Usage and working along Chat GPT

Imagine using SQL, R, Python, Julia, or Scala, for your daily data science work. And you can practically ask Chat GPT anything and it will return you a relatively coherent and good answer. If you need an explanation, it will excel. Where and what have you used it for? Here is a short list, that might get you started:

Explain the data science algorithn?
Help tune or create SQL code to query big data
Prepare R, Python, Scala code for exploring the data
Help you prepare the training of the model in desired language
Prepare the code for hyperparameter tunning and cross-validation
Ask for data visualization for given dataset
Help create dashboard
Create code for model deployment, model re-training or model consumption
Ask for preparing custom functions and algorithm/function adjustments?

Now, that you have added and found the list of where and how it did help you, I would like to understand, how did this help you? Feel free to make a general comparison and add some explanations. And lastly, of course, add, if this has in any kind of way compromise your work as a data scientist (in a term of embracing it in – a positive way, or in terms of a negative experience).

Responsible usage

We have seen many controversies around Chat GPT emerge. Some European Union countries have banned it, and some will so be doing it too. And the question is not only its use (as the end of humanity and empathy) but also the misuse of personal data, privacy issues and leaking of relevant, corporate information.

Have you considered responsible usage of Chat GPT? Here is again the short list for helping you:

The use of personal data retrieved from the model
Inserting sensitive (personal or company) data
Explaining the section of R, Python, Scala code, that is the property of your enterprise

Instead of this, have you tried using it more responsibly:

Using pseudo code for explanation of the algorithm
Using mock data rather than real data
Giving pseudo-code in order to receive the documentation
Skipping on sensible data (SQL schema, model information, sensible data)

So which cases have you come across? Did it have any consequences for you? Which other responsible use of Chat GPT have you also done?

My takeaways

ChatGPT offers interesting answers (based on my experience and search), and it is the next step from a google search of Stackoverflow. In other words, it gives you a more focused answer. When exploring and searching forums, you might find several different solutions for a single problem, whereas here, you have to ask for another solution. And respectively, it can give you answer faster, in comparison to browsing the web. In both cases, both sides have their advantages and disadvantages, but non will assure you, that the answer is correct!

I embrace this technology as an additional learning source. But I personally do not use it as my daily driver, despite trying it out a couple of times (with mixed results; working and nonworking/useless/meaningless). It can be super helpful for entry/junior positions, but the more experienced you are, the more abstract data science work you and the more complicated topics you cover, less frequently you will presumably use it.

T-SQL Tuesday #161 – Having Fun with T-SQL

Invitation and writeup from Reitse Eskens.

So, what to blog about this month. Well, it’s just been April fools day and as you’re reading this, you survived. Congratulations! But it did spark a question; what fun are we having with our code? And I’m not talking about commit messages in the Git repository or funny comments inside the code. I’m just as guilty on that part as the next programmer, but I’d like to focus on something else.

What are your most fun script, procedures or statements that you’ve written. Let me give two examples to set a bit of a stage.

The first fun script I wrote is one that has some history with it. About ten years ago when my wife was pregnant we were in the garden discussing the future. We were pulling out some weeds, trimming back some plants and enjoying the spring weather. For some reason we got onto the long term future and there a long running joke emerged. Our kid would have 18 years with us, when he would turn 18, the main present would be a set of moving boxes. Let’s call it a hint. Every now and then the joke serves it’s purpose to as a lightning rod when things don’t go like we like. The remark “well, only X years to go” relieves some of the stress. Nothing more serious than that. Until some co-workers got wind of the joke and asked for more precision. So, I wrote a very small piece of code that resulted in a number of results, the amount of years, day, hours, minutes and seconds until his 18th birthday.

CREATE OR ALTER PROCEDURE sp_howlong
AS
DECLARE @birthdate DATETIME;
SET @birthdate = '2013-01-01 00:00:00'; -- enter the correct birthday here
SET @birthdate = DATEADD (YEAR, 18, @birthdate);
WITH getData
AS ( SELECT
CONVERT (VARCHAR(12), DATEDIFF (SECOND, SYSDATETIME (), @birthdate) / 60 / 60 / 24 / 365) AS ' Year(s) '
, +CONVERT (VARCHAR(12), DATEDIFF (SECOND, SYSDATETIME (), @birthdate) / 60 / 60 / 24 % 365) AS ' Day(s) '
, +CONVERT (VARCHAR(12), DATEDIFF (SECOND, SYSDATETIME (), @birthdate) / 60 / 60 % 24) AS ' Hour(s) '
, +CONVERT (VARCHAR(2), DATEDIFF (SECOND, SYSDATETIME (), @birthdate) / 60 % 60) AS ' Minute(s) '
, +CONVERT (VARCHAR(2), DATEDIFF (SECOND, SYSDATETIME (), @birthdate) % 60) AS ' Second(s) ')
SELECT CONCAT_WS (':', [ Year(s) ], [ Day(s) ], [ Hour(s) ], [ Minute(s) ], [ Second(s) ]) AS [This is how long…]
FROM getData;

What this procedure does is getting the birthdate as it happened, adding 18 years because that’s the target. The select then calculates the differences based on on the modulo function (the % sign). As I’m converting to seconds, I can work my way down from years to seconds by changing the modulo.

I’ve used this technique in some customer cases as well to determine if a certain record had expired its valid date or not.

The second one is more work related but fun nonetheless. It’s one I didn’t think of myself but it was heavily inspired on the work from Brent Ozar. I’m a great believer in attribution, and as this is mostly his work, check out the link to get a quick working setup and adjust it to your needs.

The reason for this script came from a customer who wanted to know if all databases were up and running if a server went into a failover, reboot or whatever. We discussed the issue shortly and, having paid attention in classes of Brent, I came up with a procedure that runs after startup and checks the state of all the databases. If all the databases are online and running, it will send an email stating everything is OK. If one of the databases didn’t get to the normal state, the email will have a line for each database with the state it was in when the procedure ran. Of course, this isn’t watertight and fails if either the mailserver is down or the server never returns to normal running, but that is being monitored elsewhere.

Now this script has been running for years and just one simple ‘out of order’ message has been seen: Database Offline. Every other database has resumed without hesitation or error. Yes, some database servers are just summer children.

So without further ado, time to hit your keyboard and write about your funny scripts, code.

T-SQL Tuesday #160: Microsoft OpenAI Wishlist

Invitation and round-up from Damien Jones.

Introduction

Artificial Intelligence has been a big deal in recent months. One of the main drivers of this has been OpenAI, whose DALL-E 2 and ChatGPT services have seen extraordinary public interest and participation.

ChatGPT is currently the fastest-growing consumer application in history It reached 100 million users in its first two months, and has been integrated into numerous applications. One such example is the recent version of DBeaver that I tried out in my previous post.

Microsoft has been one of OpenAI’s most prominent supporters. In July 2019 Microsoft invested $1 billion in OpenAI and became their exclusive cloud provider.

In January 2023 Microsoft announced the latest phase of its multibillion-dollar investment partnership with OpenAI and the general availability of Azure OpenAI Service. Since then, Microsoft announced that it is building AI technology into Microsoft Bing, Edge and Microsoft 365.

My invitation for this month’s T-SQL Tuesday is:

What is on your wishlist for the partnership between Microsoft and OpenAI?

This can include all Microsoft products and services, like:

T-SQL Tuesday #159 – What’s Your Favorite New Feature?

Invitation and wrap up from Deepthi Goguri.

This month, I am inviting everyone to blog about two topics:

Blog about your new favorite feature in SQL Server 2022 or in Azure. Why is it your favorite feature and what are your experiences and learnings from exploring this feature? If you have not explored these new features yet, No worries! Blog about the features you feel interested in exploring.
New year, New Resolutions. What are your new year resolutions and how do you keep the discipline doing it day after day? Here are some examples: new hobby, plan to spend more time doing physical activity, wanted to read list of books (Please mention the names so it may also inspire others to read those books), journaling or any other resolutions you plan for this year.

Here are my answers to above questions:

I am looking forward to learn about my favorite feature Query Store and its advancements in the SQL Server 2022. Query Store feature now supports the read only replicas from availability groups. The other advancement in Query Store is Query Store hints. I have written a blog post about it here. The other new feature is the parameter sensitive plan optimization where multiple plans are stores in plan cache for a single stored procedure reducing the parameter sniffing problems.
This year, my resolution is to include exercise to my daily routine and reading David Goggin’s book all over again “Can’t Hurt me” before I begin to read his second book “Never finished”. It is getting harder to keep the exercise discipline. I had my gaps but I know I will get into the track again. I believe it is all about doing your best when you feel the worst. I am looking forward to listen to your resolutions and your discipline in following them day in and day out.

If you are looking for the latest features in SQL Server 2022, follow this series of videos by Bob Ward and Anna Hoffman explaining the new capabilities and features for SQL Server 2022. For new features in Azure, please check Azure SQL updates here and general overall Azure updates here.

T-SQL Tuesday #158, Implementing Worst Practices

Invitation from Raul Gonzalez.

One of the most repeated answers to almost any question asked within the SQL Server community is that “everything depends”… Can that also apply to known best practices?

Furthermore, is it possible that some of the commonly agreed “worst practices” have indeed some use case where they can be useful or suit an edge use case?

This month I am asking you to write about those not-so-common practices that you may have implemented at some point and the reasons behind it, I have a few in my pocket that will make more than one a bit uncomfortable