What factors other than some specific affinity to Microsoft makes a company choose Azure over AWS?
For us, Azure was a big win for security compliance and data sovereignty. Those factors triggered the switch.
After the switch, we came to realize that Azure is much stronger in support and in the ability to interface with the Azure product teams to help guide the direction of the platform.
I've seen some recent reports that Azure is growing twice as fast as AWS so I wouldn't count them out by any means.
Yes.
> What factors other than some specific affinity to Microsoft makes a company choose Azure over AWS?
Azure has a decent offering even if you don't have any "specific affinity to Microsoft". You can use Azure to develop products that don't use any Microsoft technologies. Although from my own experience, Azure tends to be a bit rough around the edges especially when used with non-Microsoft tools and languages.
See marketing page: https://azure.microsoft.com/en-us/overview/azure-vs-aws/
We have data residency requirements to keep things in Canada. This actually forced us to migrate off AWS, first to IBM, then eventually to Azure.
https://aws.amazon.com/blogs/aws/now-open-aws-canada-central...
CosmosDB was the difference for us.
What do you mean by Microsoft-specific stuff? I run a bunch of stuff there. When I started using these things their PaaS offerings were much more mature than the AWS counterpart (since they focused more on IaaS).
More locations, tho' every major cloud is opening more all the time. A much better hybrid story, tho' this may change once (if) AWS get their alliance with VMware working properly. Better layered applications (X-as-a-service) but that might be a matter of taste - AWS is more about giving you building blocks to build your things, Azure has the blocks but is better at giving you integrated "things" out of the box (e.g. IaaS vs PaaS). Per-second billing, generally better budget control.
I've a bit of experience with both Azure and AWS. There's nothing I could build in one that I couldn't build in the other, tho' depending on what that is, it might be more effort in one of them. For my own personal stuff, I use Azure, it's more fun :-)
I have ran into scale problems very fast at Google and now am having to use App Engine and add more complication which I don't have personal engineering skill/capacity.
Google support first bumped me up to 12000 max queries per 100 seconds and said that's the limit, but that's not close to enough. We have super bursty traffic (webhooks that aren't concatenated from source).
Google then increased to 100,000 per 100 seconds after they claimed 12k was maximum.
It's still not enough and I think their sampling might be funky because when I look at the invocation/traffic graphs it doesn't align with the stated maximum (still throws quota errors).
Kind of frustrating. The idea of stateless functions without worrying about engineering is a dream! I don't get why Google can't get 'google scale' here. In the past we've used Snowplow on AWS when we had engineering help but that's a huge amount of work/engineering time. The actual accept POST, store to cloud storage, insert to DB is less than 100 rows.
Response time is also pretty bad in the extreme cases. Even though I end the response before processing data I still see 95%/99% spikes when bursting on the far less bursty data source (as in only bursting to 10/second). Just today for this function I got 3764ms 99%!
We're internally using pubsub (and nats) sending billions of messages every day. We don't use the push mode as Spotify [0] but pull from the queue instead which allows us to run on our pace.
We do write into bigquery (and citus) and each components is doing it on their own pace based on what they're capable of process at the moment.
[0] https://labs.spotify.com/2017/11/20/autoscaling-pub-sub-cons...
Counter-intuitively, ending the response before processing the data may actually be hurting your p99 response time. According to [3], instances which are not currently handling a request get very little CPU and generally don't make any progress. This means that this work will still be waiting to be done when the next request comes in and can slow it down. You may even end up with an instance trying to process several requests' data and also trying to handle another request on top of that. This last request is going to get an overloaded instance and likely be very slow. Further, the function isn't guaranteed to ever be run again, so that deferred work might not even ever happen.
It probably will cause you to use more of your invocations per 100 seconds quota, but you really shouldn't start background processing tasks in a Google Cloud Function that continue after completing a request.
[1] https://cloud.google.com/functions/quotas [2] https://issuetracker.google.com/issues/new?component=187195&... [3] https://cloud.google.com/functions/docs/bestpractices/tips
Interesting and thanks I had no idea on the end response CPU time. It's just storing the json to Google Cloud Storage but it does take 100-300ms it seems!
It's also why I think that Facebook never joined the rat race. Most of what they do has no relevance to the average company.
Amazon, an online retailer, was really the most natural place to start an outsourcing of banal technologies for the largest market segment. And has grown from there.
