diogofranco

Yes but I'm saying that they will still own Wiz at the 10 year mark, so you can discount their valuation at the time from the 100B.

I don't think that 100B number is correct. It would be if Google had to give back the business (or it imploded) after 10 years

"Was the non profit a way to just avoid tax until the time came to start making money?"

You'd want to do it the other way around

Do you live in the EU? From where I stand I mostly got cookie banners, can't get to Google maps or flights from Google search, get AI launches 6 months after the rest of the world if at all etc.. Not yet feeling all the consumer benefits

> by creating an SEO industry that's 100% focused on optimizing for their ranking signals.

The SEO industry will optimize towards ranking on whatever search engine people are using

Buybacks "pumping the stock price" is a common misconception, but not actually true (unless the buyback is executed at prices lower than fair value).

There are less shares outstanding afterwards, but there is also less cash, so if executed at fair prices there is no reason for the stock price to move.

Just think through an example of a simple company which is a pot of $1000, and can get 10% returns on it. There are 100 shares, so if investors want 10% returns, shares should trade at 10$ (P/B = 1). If the company repurchases half the shares, there will be only 50 shares but the company only has 500$ now, making each share still worth 10$ for 10% hurdle rates.

Bard is the consumer product, Gemini the new model behind it

But delete/rename columns are not the only operations, nor the most common. Adding columns is the most common, and there only one of those options breaks

It made less than no money recently. You might be thinking of revenue.

I find the "you're not paying enough" argument a little strange. By definition, not every company can pay top of market and get the most skilled engineers.

If the average engineer doesn't have a certain skill, I think it's fair to say that it's hard to hire for it, because it will be for the average company.

There is no last percent, it can drop another 99% and then another...

Interesting additions! On using column aliases in predicates, what if my alias exists in the source as well, what takes precedence? I feel like this can become a bit confusing either way.

But revenue isn't really interesting, is it? Amazon won't pay the fine in lost revenue, it will pay it with profits.

This is completely wrong, 1B is more like 1/3 of Amazon's global profits last quarter.

They might never profit enough from Italy to make up for this.

I meant for Google, where it is. Working from home or the office doesn't really affect compensation, location is the variable.

Location is in the contract, you sign a new offer in order to move

This, many companies even make this quite explicit by issuing stock to employees and buying back the same number of shares, in order to pay with cash flow rather than dilution.

And in the other direction as well. I live in the EU and used to buy clothing from a UK e-store which would trivially arrive a few days later. This year, my 35eur shirt got stuck in customs and I had to pay another 40 to retrieve it (vat + customs fee). That was the last time I'll be buying online from UK.

Agree that this is a bit confusing. I ended up writing a small guide on how date arguments work in airflow (https://diogoalexandrefranco.github.io/about-airflow-date-ma...) and I always end up consulting it myself, as I just can't seem to memorize any of these macros.

Besides the BCH part of that quote, the "fans" part is also interesting. Coins having fans is... new.

> I'm arguing that they haven't made a more entertaining version, they're confusing novelty with entertainment

Is your position that disallowing castling does not lead to less king safety and thus less draws, more mating attacks, piece sacs and complex tactical games? Or is it that you think these things do not contribute to more entertaining games?

Surely there are many reasons to not hoard everything, but I find this argument strange. If you're in a business that is making bad decisions due to wrong analysis of data, it seems like there should be more nuanced and pertinent ways to tackle this then just throwing it out, which also prevents good analysis.

I can understand the worry about big tech due to the scale, but on a personal decision like that, I'm much more worried about giving my data away to an ecobee type company.

I don't think your example depends on the person's circunstances, there is a very clear cut rational decision there. Even not taking the utility of money into account, nor the fact that you need to discount that million as it only arrives in 10 years, you are still only making 10k on average on that gamble. Taking the 300k is the rational decision.

But it does operate this way, of course. It is just the same effect that causes "earth society" to mostly show results about the superstring "flat earth society"

Agreed (OP here). Although I never studied them personally, I often hear that Ralph Kimball's books on data warehousing and ETL are the canonical texts on the side of data engineering you mention.

I wrote a simple blog post recently with links to data engineering resources I found useful to get into the field, hopefully it is helpful: https://diogoalexandrefranco.github.io/data-engineering-reso...

csv is notoriously bad with spark - in fact you couldn't write to csv for the longest time without external libraries like the databricks one, so I don't contest your last point.

SQlite is usually a single file representing a database though, I don't know how it would work with partitioning and stuff, and then how to handle the schema evolving across sqlite files.

> Why aren't we replacing CSV with it, especially for big data applications?

Big data applications tend to use other structured binary formats like parquet and avro, which any big data tool can typically parse.

pyspark might be the go-to language for data scientists playing with the spark repl, or MLLib, but for production data engineering, scala is still king.

Besides performance and the obvious fact that not knowing scala makes it difficult to understand the underlying Spark code, there are multiple ways in which scala is more natural to develop in (many libraries are for scala only, for example).