There are at least 7 Amazon-related stories on the HN front page right now, what’s going on?
Merry christmas?
Amazon Timestream – Fast, scalable, fully managed time series database
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Re: Amazon Timestream – Fast, scalable, fully managed time series database
#12There are at least 7 Amazon-related stories on the HN front page right now, what’s going on?
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Re: Amazon Timestream – Fast, scalable, fully managed time series database
#13There are at least 7 Amazon-related stories on the HN front page right now, what’s going on?
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Re: Amazon Timestream – Fast, scalable, fully managed time series database
#14jesus christ six amazon articles in a day? AWS is undeniably the body of christ for HN but am i missing something?
FSX, blockchain, timestream, Graviton, ground station, and cloudwatch... all of these articles are advertisements for mundane shit.
Re: Amazon Timestream – Fast, scalable, fully managed time series database
#15jesus christ six amazon articles in a day? AWS is undeniably the body of christ for HN but am i missing something? FSX, blockchain, timestream, Graviton, ground station, and cloudwatch... all of these articles are advertisements for mundane shit.
https://reinvent.awsevents.com/
It happens every year during Google, Apple, Amazon, and Facebook events.
Re: Amazon Timestream – Fast, scalable, fully managed time series database
#16So what will this compare wrt boundary, signalfx, stackdriver, etc types of previous services...
Ill have to go look into this, because if aws historic pricing for any large volume stream, quickly becomes untennable.
Its very easy to have gobs and gobs of time series points... aws might make using this way too expensive for anything at relative scale for a small startup?
Re: Amazon Timestream – Fast, scalable, fully managed time series database
#17Honest question: when dealing with time-series data, do you actually need every data point? Is that level of granularity really necessary? IMO, it makes way more sense to decide the aggregations you want ahead of time (e.g. "SELECT customer, sum(value) FROM purchases GROUP BY customer"). That way, you deal with substantially less data and everything becomes a whole lot simpler.
Really depends on the use case. Working in healthcare, vital signs can be modeled as time series points, but are lower frequency than, say, metrics from servers. However we want to store every point so a spike is not missed. One could argue an unsustained spike is noise, but in the healthcare domain there may be a correlation with some external event (the purpose is surprised and their heart rate spikes).
Re: Amazon Timestream – Fast, scalable, fully managed time series database
#18jesus christ six amazon articles in a day? AWS is undeniably the body of christ for HN but am i missing something? FSX, blockchain, timestream, Graviton, ground station, and cloudwatch... all of these articles are advertisements for mundane shit.
https://reinvent.awsevents.com/ It happens every year during Google, Apple, Amazon, and Facebook events.
thanks. Just glanced at the shop calendar and i guess the holidays are right around the corner too.
Re: Amazon Timestream – Fast, scalable, fully managed time series database
#19jesus christ six amazon articles in a day? AWS is undeniably the body of christ for HN but am i missing something? FSX, blockchain, timestream, Graviton, ground station, and cloudwatch... all of these articles are advertisements for mundane shit.
It's AWS re:Invent day/week. I've never been, but I get the impression that it's like Apple's keynote, or Google I/O, in which big product announcements are made. On those days, you'll see multiple submissions about the respective conferences too.
Re: Amazon Timestream – Fast, scalable, fully managed time series database
#20At my day job, I build a lot of machine learning systems that require data to be fed in a time series manner[1].
Often this means building systems to analyze terabytes of logs [semi]-realtime. All I have to say is - thank god! This is going to make my job a lot easier, and likely empower us to remove our current infrastructure setup.
I know at one point we actually considered building our own time series database. Instead, we ended up utilizing a Kafka queue with an SQL based backend after we parsed and paired down the data, because it was the only one quick enough to do the queries.
Should make a lot of the modeling I've worked on a bit easier[1].
[1] https://medium.com/capital-one-tech/batch-and-streaming-in-t...