Seems completely unnecessary, there is probably 0 overlap between people who buy a cheap MacBook and people running DuckDB locally
Big data on the cheapest MacBook
41–50 of 321 posts
Re: Big data on the cheapest MacBook
#42Seems completely unnecessary, there is probably 0 overlap between people who buy a cheap MacBook and people running DuckDB locally
I love small form factors, and I am what youd call a professionel :P
Re: Big data on the cheapest MacBook
#43I'm interested by one (not for big data) but only 8 GB or RAM is kinda really sad.
My good old LG Gram (from 2017? 2015? don't even remember) already had 24 GB of RAM. That was 10 years ago.
A decade later I cannot see myself being a laptop with 1/3rd the mem.
Re: Big data on the cheapest MacBook
#44I’ve been tempted to buy one and do “real dev work” on it just to show people it’s not this handicapped little machine. I built multiple iOS apps and went through two start up acquisitions with my M1 MBA as my primary computer, as a developer. And the neo is better than the M1 MBA. I edited my 30-45 min long 4k race videos in FCP on that air just fine.
Can you say a little more about what you mean by "better"? How much faster is editing?
Re: Big data on the cheapest MacBook
#45I would have benchmarked with an instance that has local nvme, like c8gd.4xlarge.
Do they make any promises about persistence of local NVMe after something like a full-region power outage yet?
Because if you can't do durable commit on a single-region cluster that will be just temporarily unavailable without loosing committed data if something like that happened, it's not quite there unless you still stream a WAL to storage that they do promise you will survive a full blackout of all zones that store (part of) the data.
Re: Big data on the cheapest MacBook
#46I would have benchmarked with an instance that has local nvme, like c8gd.4xlarge.
Do they make any promises about persistence of local NVMe after something like a full-region power outage yet? Because if you can't do durable commit on a single-region cluster that will be just temporarily unavailable without loosing committed data if something like that happened, it's not quite there unless you still stream a WAL to storage that they do promise you will survive a full blackout of all zones that sto…
You already lose your data after instance restart so I think that full region outage is already out of question.
Re: Big data on the cheapest MacBook
#47I think it’s relevant to first read [1] to see why they’re doing this. It’s basically done as a meme.
Re: Big data on the cheapest MacBook
#48as a broke ecologist, this little computer can do everything I need in R and word and is a phenomenal build for the price. I'm really enjoying it thus far.
Re: Big data on the cheapest MacBook
#49I’ve been tempted to buy one and do “real dev work” on it just to show people it’s not this handicapped little machine. I built multiple iOS apps and went through two start up acquisitions with my M1 MBA as my primary computer, as a developer. And the neo is better than the M1 MBA. I edited my 30-45 min long 4k race videos in FCP on that air just fine.
I just retired my m1 air to being a server this month. They’re very capable laptops. If the neo is even comparable in spec it’s excellent for the price
Re: Big data on the cheapest MacBook
#50This is as much an indictment of AWS compute as it is anything else.
Yeah, this is really about how ludicrously overpriced big cloud is. I’ve got a first gen M1 Max and it destroys all but the largest cloud instances (that cost its entire current market value per month!), at least in compute. It’s a laptop! A decent bare metal server in a rack will destroy any laptop. It’s staggering. Jaw dropping. Bandwidth is even worse, like 10000X markup. Yet cloud is how we do things. There’s a g…
I agree and disagree, the benefit with cloud is you "don't need to manage it", it scales automatically, redundancy, and automatic backups etc. I do think you are right; in the future there will be more infrastructure as code as cost pressures become more obvious.