(I'm italian) Some international pasta brands (e.g. Barilla) have different cooking times depending on the country/market. I moved from Italy to Germany and noticed the +1 minute in many types of pasta, so I always compensate. I find the cooking time precise and useful, I don't get why people want to reinvent the wheel.
When I worked in a restaurant, we would pre-cook the pasta in the morning, store it and then cook the last few minutes when the order came in (this is actually very common also in good restaurants). Barilla/DeCecco sell restaurant targeted packages and they also report the cooking time split with the precooking. Results are basically the same
Behind this there is the wrong assumption that there must be a replacement for all.
For sure there will be gaps filled by other powers, but it's not necessary, we just got used to it.
What do you mean for "behave", there are a lot of usecases that require more than 8GB memory (expecially for developers) and there is no CPU magic that will help you with that.
Interesting, but still I don't think your comment is very relevant to this. Without cows we could have a forest instead of grass, instead of leaving it to rot. So if the number of cows remains constant you benefit from reducing methane emissions.
If you don't need the sea, I suggest some cheap german city like Leipzig or Dresden.
Another not so common idea is Bulgaria: cheap, very low taxes (flat 10%) and a lot of english speakers (expecially in Sofia). I've been there several times and it's also a very nice place.
If there was something better than Spark for distributed processing, we would be using it. The rest of your comment is a straw man argument, assuming everybody uses it for datasets fitting in memory of a single node.
For research, I created experimental RDF storage on top of Parquet and Apache Spark for querying big graphs[1].
It converts the RDF graph in a sort of property graph, where we have a row for each entity and where the columns are the all possible properties.
The trick is to use a columnar format with the proper encoding (in our case Parquet), to solve the problem of having a lot of columns and a huge NULLs space. With this representation we can eliminate costly joins for most of the common queries, but also reduce the size of the necessary ones.
We use Lambda with S3 as intermediate storage between different steps, sort of multiple-stages map-only MapReduce. And we still use Hadoop on premise :)
Wikipedia spends more than that only for "Donation processing expenses". Of course it is a nice contribution but, in my opinion, very small if compared to the huge value that Google is able to get for free.
https://wikimediafoundation.org/about/2018-annual-report/fin...
Tipo 00 is not special but very common flour n. 405 (german system).
Many tutorials refer to this flour like there is something special inside that will make the pizza better...but there isn't.
>In the Netherlands, seasonal influenza has ~1% mortality rate -- in the U.S. it's more like 0.1%
Completely false, it seems 0.1% also in the Netherlands.
"In the Netherlands, our closest comparisons are the Mexican flu of 2009/2010, with a mortality rate of just 0.02%, and the regular flu epidemics, with an average mortality rate of 0.1%"
from:
https://healthcare-in-europe.com/en/news/on-the-implications...
Under 10 seconds on which query? If your query involves big joins over a large distributed data-set there won't be a technology that can do it for you. SPARQL is not the problem, you can write the same queries in Cypher or any other language, you will hit the same performance problems
The only way to test is if you talk with an expert and he says you have understood. There are many things in linear algebra that you can use in practice even when you didn't really understand them.
This is the reason why self-studying certain topics is very hard, you still need (good) teachers to give you constant feedback.