The hype about “tech” that costs money each time you use the tech is an interesting race people are excited about.
However who are the people? Are they biased? Have any of them gained an #informationsystems degree? Or maybe they were a sales rep for an #analytics company? Do they know 1nf, 2nf, 3nf? Do they understand relational theory?
Like, have they installed mysql on their local computer and lost the root password? Do they understand how to aggregate #data? Or do they pitch 100k tech because they can’t write #SQL….
For me, personally speaking, I don’t get caught up in all the hype by strangers who can only change values in a SQL query, text editors. If they act like anything but an artist.
I may rank for tableau consulting services, however it doesn’t mean I’m calling myself a master of tableau or seeking to gain marks on my bed post that says I can use a software.
I see a lot of people hyping cloud databases and SQL products…
I see a lot of people hyping cloud databases and SQL products, these people have no production data solutions or experiences, and the only people who like the content on LinkedIn work at the company… lol
This is false excitement, a balloon of hope that will be iced as soon as the business hires someone with experience writing basic SQL.
I think most engineers using cloud tech are not into the hype because a lot of cloud tech is becoming tedious to use and most people who use them in a technical space do not enjoy using the products unless they get $$ incentives. The people most excited about cloud #databases don’t know SQL. If they knew sql, they wouldn’t need cloud databases in 99% of the use cases being thrown at the company.
I do not understand the hype, I understand the SQL which means I have never needed to suggest cloud databases or sql products that offer buzzlightyear speeds.
Rather, I understand the reason for databases, it took awhile to understand hype is hype, solutions are solutions, and sales people suck at SQL, which doesn’t mean they can’t post exciting pictures about practically nothing, maybe an animal running fast, wow thrilling, and at tag some company too. Wowzers.
I’m bored by linkedin content, and maybe I am the only person bored of this tech hype on linkedin by thought leaders who can’t write any SQL? Or maybe my linkedin #algorithm like feeding me junk posts?
How much money could be saved on cloud databases if you learned #SQL?
I’m not jumping into the #clouddatabase hype because I didn’t go to college to make #dataanalytics anything but small and what business requirements require.
I’ve seen countless businesses in the past 10 years who have bought into insane cloud database tech because the person selling it can’t write a lick of SQL.
Same for analytics apps and suggestions for #ETL projects, it’s been a rough decade, a busy one, and I believe it’s mostly due to the fact that most people are not willing to learn basic #database tech or information system technology.
What I see is… They will make some edits to sql, use a few apps, and call themselves experts or worse, thought leaders.
Problems; data sizes are increasing and those “solutions” are becoming a burden. Proof of concept, DEMO tech, is good for sales, not good for consulting or production. This is why a lot of consultancies burn out if no one has any understanding of database tech. I know from experience.*
I understand a lot of people think we need to query a LAKE… or whatever you want to call it today.
HOWEVER, Why purchase #cloud database solutions if you’re still learning what a database is designed to do for you?
If you haven’t defined 1nf, 2nf, and worked through the reasoning for a database, your solutions will continue to be inefficient technical debts that require nonstop project work.
Question for you; Do you really need to query a trillion records?
My 2cents; 99% of the time you will not need more than a few hundred thousand rows, and this is in the worst case scenario.
Do you need trillions, or is the business only looking at quarterly results? Is anyone asking these questions? Or is relational theory common sense around people who don’t know the differences between star schema and a relational flow diagram.
Why store trillions of records over and over, back ups on back ups on environments, on DR, and backups on backups… Why store trillions of records when 100 will do?
A lot of my success is built on the fact that I’m willing to suggest what’s available maybe better if we analyze the problem again.
Did the developers make a bad app that collects unnecessary data that requires hard SQL? Is it CRAZY all the time, your #SQL? Likely we can focus here VS the analytics apps and making charts that require complex SQL to maintain.
Often times solutions can get off the rails because 1 person, who never wrote SQL, suggests an app 5 years ago.
However in 10 years, what will that app and the #solutions cost you from a support perspective? Is cloud data really necessary or did you get excited and purchased that big sku?
A lot of these questions will be asked about your environments, solutions, #apps, and I won’t be the only person questioning the solutions.
Or read more about me rambling for no reason about functional results because I guess I’ve been inspired to write a little bit lately.