Yeah, the rebranding ETL is ELT is funny because it just moves instead of processing the data. I don't know, in Spark or wherever you're going to process it beforehand or define your schema, you're just dumping it somewhere else and then defining it later. So they own the computer there. But you know, overall, I think the main culprit in this is that a lot of companies aren't educated on what it looks like to succeed.
Snowflake for all the great things that they've done and in Google Cloud as well as BigQuery, they've lowered the bar and lowered the entry point of what it takes for a company to get started. They want the data now. They want to build some analytics.
They wanna build some reports and things, and that is that's great, but if you don't design stuff correctly upfront, you're taking a payday loan at 30% interest, or in the case of Snowflake, I don't know what are they, 60% NDR or something. You're taking a 60% loan on average and 60% like interest.
And this adds up like it adds up in the second year. It adds up in the third year. A lot of it comes down to SQL. What we've seen here is the rise of SQL, in the last couple of years. SQL is easier to learn than an object-oriented programming language. Using declarative SQL to define your data schema in Snowflake in a database, yeah, it works fine.
It works great. If you're not writing optimal SQL and you're doing things like joining on high cardinality data that can add up very quickly if you're doing like looping functions or nested loops in SQL. That's gonna add up and a lot of people don't think about this, but I think a lot more like both individual contributors, data directors, and managers need to think about this more about the exponential growth.
If you're running some kind of function, you know right now, like it can cost you like, I don't know, one snowflake credit next period, it can cost you two snowflake credits when your volume goes up and you're running it. Now, if you're doing it linearly, it should take you three. It costs you three credits next time.
But if it's growing exponentially, it's not gonna cost you three. It's gonna cost you four. And then next time it's gonna cost you eight. You're gonna double it every time cuz you're making all of these, N-squared types of solutions, and I've seen this pretty constantly. And the same with indexing data or figuring out how data's partitioned.
A lot of basic stuff falls by the wayside because of the desire to move fast and moving fast is good. And moving fast is the name of the game for a lot of these tech companies in the last few years when it's all been run, run, run, but now, it's time to optimize a little bit.
And, I even wrote a piece recently on my Medium about, Snowflake as a table game. But the Google Cloud ecosystem is a casino. And, if we're talking about things that are wrong or maybe look off one of the funniest things here, and I love Google. I like BigQuery a lot, and I like their ecosystem.
I like all their solutions and use them all the time. But one thing I've gone over throughout my career is with these tech companies like they'll start using Google Analytics, they'll start doing Google paid ads, and like that. If they're in consumers, like they're selling consumer goods to individuals, they're definitely using Google Ads, Facebook ads, and everything else.
Google ones can be big. Same with some B2B but they're using all these Google, they're paying. Google has lots of money for all these like applications, Firebase for their app, and they're collecting all kinds of data. And they get to a certain point when they say, wow, I'm spending so much money on Firebase and Google ads.
Like, why are we spending so much on Google Ads? We shouldn't be paying Google so much money. We need to get our tax to improve our customer acquisition costs, we shouldn't be, we should be targeting better customers. And so then what do they do? Everything plugs in nicely to the Google cloud platform like all of Google's products.
So then they hire machine learning analytics, and data engineering to work on Google Cloud. They get their tax down. They might pay Google ads less money, but now they're paying Google like BigQuery, and Google Cloud more. So Google, even though Google goes down over here on the ads. They go up on what they're taking out of these companies on the compute side of putting together dashboards, machine learning, and everything else to improve what they're spending on other Google products.
And I think that's just really funny overall and I think a lot of people have not thought about that fully. But it's definitely like what the game is. So, very funny stuff.